Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review
Abstract
:1. Introduction
2. Methods
3. Results and Discussion
3.1. Signal Preprocessing
3.2. Signal Processing
3.2.1. Time Domain Analysis
3.2.2. Frequency Domain Analysis
- The Fast Fourier Transform (FFT):The Fast Fourier Transform (FFT) is a mathematical tool and algorithm that decomposes a signal into a combination of sinusoidal functions possessing different frequencies. This method is a classical signal processing technique with prominence in identifying the generic frequency characteristics of signals. The studies referenced as [20,23,27,28,29,30,44,45,46,69,88,99,103,104,105,106,107,108,110,111,112,143,144,145,146,179,180,181,182,183,184,185,216,217,218,219,220] leveraged the advantages of the FFT for different applications, namely for rotating machinery diagnosis, predicting the remaining useful life (RUL) of bearings, and the maintenance of electrical machines.Nevertheless, the assumptions on which the FFT is founded give rise to several restrictions. First, it presumes that all the signals to be analyzed are continuous. The signals, however, are mostly discrete and sampled after undergoing a preprocessing phase. Another assumption in the Fourier transform is that the signal should be stationary. That is, its statistical properties remain constant with time. Again, these assumptions are often violated by real-life signals, which show non-stationary behavior. Furthermore, the FFT considers its input signal as periodic and linear. While this may be true for some signals, in most cases, it does not really explain the complexity of most real-world signals.
- Power spectral density (PSD):Power spectral density (PSD) refers to the representation of the amount of power that exists at each frequency band of a signal [283]. It is suitable for analyzing signals whose energy is spread over a range of frequencies rather than being concentrated within a few frequencies. The area under the PSD curve over a frequency range gives the total power of the signal in that range. In the papers cited as [88,113,142], PSD was useful for the analysis of transient signals such as EEG and speech.
- Cepstrum:The cepstrum is a signal processing technique used when the frequency-based information of the signal, like the harmonics, needs to be examined separately. It is obtained by taking the inverse Fourier transform of the logarithm of the Fourier transform of a signal. On account of this, the signal is studied in the “quefrency” domain, where the quefrency denotes delays or periodicity in the original signal. This technique was used in the works of [100,142,147,148] to extract Mel frequency cepstral coefficients (MFCCs) as characteristics for the evaluation of signal health.
- Envelope spectrum analysis:Envelope spectrum analysis (ESA) is a highly effective method for detecting modulating patterns within a signal. By applying a Fourier transform to the envelope signal, a smoothed and time-variant version of the signal’s amplitude is obtained. In practical applications, envelope spectrum analysis, including spectral kurtosis analysis, has been found to be more advantageous than traditional raw vibration analysis for early-stage fault detection and anomaly identification. This is because many types of machinery faults and defects manifest as fluctuations in amplitude in vibration signals. Several studies, as referenced in [47,56,89,90,97,98,149,221], have leveraged ESA for fault diagnosis and prediction in bearings.
- Other:In the study [57], Singular Spectrum Analysis (SSA) was used for decomposition of a signal into its fundamental parts to investigate trends, oscillations, and noise for the diagnosis of bearing faults while the study [106] used the Fractional Fourier Transform to extract the properties of vibration signals.Another study [27] used the Butterworth filter to refine the EEG signal by removing unwanted frequencies and thereby predicted epileptic seizures. Moreover, the implementation of modulation signal bispectrum analysis (MSB) in [91] yielded precise information regarding the modulation properties of the signal for gear monitoring.
3.2.3. Time–Frequency Analysis
- The Short-Time Fourier Transform (STFT):The Short-Time Fourier Transform (STFT) is a mathematical technique that operates by dividing a longer time signal into shorter segments of an equal length and then computing the Fourier transform separately for each short segment, expressing the variation in the signal frequency in that segment over time [284]. This process captures both the temporal and frequency information, providing a three-dimensional representation of the signal. The STFT is recognized for its straightforwardness and clear physical explanation, which validates its application in the research works cited as [29,31,92,99,100,111,112,114,115,147,182,187,218,222,223].
- The S-transform:The S-transform, an extension of the Short-Time Fourier Transform (STFT), addresses certain limitations of the STFT, including the cross-term problem. It is obtained by convolving the FFT transformed signal with a Gaussian window function. Researchers used the S-transform in [106,189], respectively, for fault diagnosis in rotating machinery and for the classification of high-frequency oscillations in intracranial EEG signals. Nevertheless, the S-transform may pose computational challenges, particularly with extensive datasets, and may exhibit redundancy by offering excessive information.
- Wavelets in the time–frequency domain:Signal transformation using wavelets is considered a versatile tool that can be adapted to each of the analysis domains. For adaptive time–frequency analysis of non-stationary signals, the wavelet transform (WT) decomposes the signal of interest into a set of basic localized waveforms called wavelets. A signal is analyzed by examining the coefficients of its wavelets [285]. In the time–frequency domain, wavelet analysis reveals the frequency components of signals, just like the Fourier transform, but it also identifies where a certain frequency exists in the temporal or spatial domain. Additionally, wavelet transforms have the capability to compress or denoise a signal with minimal loss in quality. Numerous studies [27,88,105,106,142,150,188,215,224,286] have utilized the wavelet transform as a valuable tool to characterize their signals and obtain high-quality representations in the time–frequency domain.
- The Wigner–Ville distribution (WVD):In contrast to linear representations, the Wigner–Ville distribution (WVD) is a quadratic time–frequency distribution with broad applications in signal processing and spectral analysis. It is defined as the expected value of the product of two versions of a signal that are shifted in time and frequency [287]. Belonging to the Cohen class of time–frequency distributions, the Wigner–Ville distribution offers advantageous properties, such as marginal distribution and localization in the time–frequency domain [288]. Although the WVD is functionally similar to a spectrogram, it outperforms it in terms of its temporal and frequency resolutions. This distinction resides in the principle of uncertainty in the time–frequency distribution that is not applicable to the bilinear Wigner–Ville transform, as it is not based on segmentation [289]. This method was used by the studies referenced as [32,100,225] for fault detection in wind turbine induction generators, as well as the classification of episodic memory and the prediction of heart disease in medical research.
- The Hilbert–Huang Transform (HHT):The Hilbert–Huang Transform (HHT) is a valuable signal processing method that leverages the advantages of Empirical Mode Decomposition (EMD), which will be discussed further in the time scale domain section, and Hilbert Spectral Analysis (HSA). The signal is decomposed into a limited set of intrinsic mode functions (IMFs), along with a trend component, through EMD. Hilbert Spectral Analysis (HSA) is applied to each IMF to determine the instantaneous frequency and amplitude [290]. Noteworthy studies that have utilized this method include references [88,101,151,218,226].
- The Gabor transform:The Gabor transform is a mathematical operation that examines the sinusoidal frequency and phase characteristics of a signal across time. By employing a Gaussian window function, it is able to analyze the signal in both the time and frequency domains concurrently, incorporating shifting, modulation, and power integration [291,292].
3.2.4. Time Scale Analysis
- Wavelets and the time scale domain:Time scale analysis is commonly linked with the wavelet transform. Depending on the choice of the mother wavelet, the signal is viewed across different scales. The Continuous Wavelet Transform (CWT) and the Discrete Wavelet Transform (DWT) are two commonly used techniques in wavelet analysis. The CWT provides a continuous representation of a signal in the time scale domain, while the DWT offers a discrete representation that is often preferred in practice due to its computational efficiency. The studies referenced as [45,59,71,89,100,102,117,118,119,120,143,151,152,153,154,155,156,187,192,193,215,218,227] used these wavelet transforms to analyze wind turbine signals and diagnose faults in rolling bearings, as well as extract features from EEG signals.Advanced extensions of the wavelet transform provide a richer representation of signals by decomposing both their low-frequency (approximation) and high-frequency (detail) components at each level of the transform. In contrast, the standard wavelet transform only decomposes the approximation (low-frequency) part of the signal at each level.In contrast to the standard wavelet transform, the Wavelet Packet Transform (WPT) decomposes both low-frequency and high-frequency components at each level of the transform, thereby providing a richer representation of signals. This method was employed for fault diagnosis and prognosis in rolling bearings [121,157,238], as well as detecting drowsiness on the basis of EEG signals [43].On another hand, the Empirical Wavelet Transform (EWT) adaptively decomposes a signal into different frequency bands taking into account its specific spectral content. The Fourier spectrum of the signal is divided into multiple sub-bands depending on where significant transitions occur, thus defining the boundaries between the different frequency components. A corresponding wavelet filter is constructed for each segmented sub-band. Ultimately, the signal is decomposed using empirical wavelet filters, which results in a set of wavelet coefficients that represent the different frequency bands. This method demonstrated its effectiveness in the studies referenced as [60,148,159] for fault diagnosis in planetary gearboxes and for seizure detection from EEG signals.Additionally, other wavelet-based techniques, such as dyadic and binary-tree wavelet filters [92], the second-order synchroextracting wavelet transform [48], Modified Continuous Wavelet Decomposition (MCKD) [56], and Grossmann–Morlet time scale wavelets, have also shown promising results in extracting relevant time scale features.
- Cyclostationarity:Cyclostationarity is a fundamental concept in signal processing that pertains to periodic fluctuations in the statistical characteristics of a signal. Techniques for cyclostationarity analysis serve as robust methods for identifying and understanding cyclostationary signals, which display periodic statistical behaviors. They rely on the identification of frequency shifts to identify periodic patterns in signals that are referred to as cyclic frequencies. These methods are especially beneficial in scenarios like fault diagnosis in bearings, aiding in the detection and examination of cyclostationary patterns in signals associated with equipment malfunctions [49,112,114,122,123,143,149,181,194,226,228].Within cyclostationarity analysis, cyclic spectral coherence (CSCoh) is used as a statistical metric to assess the second-order cyclostationarity of signals. It measures the linear correlation between two signals in the frequency domain, enabling the detection of cycle frequencies in diverse datasets and the identification of significant cycle frequencies in signals with cyclostationary attributes. Researchers delving into the realm of cyclostationarity have utilized CSCoh to pinpoint key cycle frequencies within signals, as noted in studies [42,106,124,195,196]. This analysis has led to a more profound comprehension of the cyclostationary nature of these signals.The CSCoh function is closely connected to the cyclic spectral correlation function (CSC), which acts as a cross-correlation function. This metric reveals the similarity between a spectrum and its adjacent spectra, shedding light on how spectra vary across positions, a point highlighted in the study [124]. Furthermore, a separate study outlined in reference [72] introduced the Cyclic Spectral Covariance Matrix (CSCM) as a tool to glean insights into the cyclostationary characteristics of transient signals.
- Adaptive decomposition techniques in the time scale domain:Adaptive decomposition techniques in signal processing are useful in complex environments with multiple sources operating on similar spectrum segments [293]. Their particularity resides in their ability to automatically adjust to the input signal’s characteristics, offering a more flexible and effective approach compared to traditional decomposition methods. For instance, Empirical Mode Decomposition (EMD) identifies intrinsic modes, and Varional Mode Decomposition (VMD) separates modes variationally, with each having distinct advantages and limitations [293].Empirical Mode Decomposition (EMD), being a data-driven method, decomposes a signal into a set of intrinsic mode functions (IMFs) based on its local characteristics. It is particularly suited to analyzing non-linear and non-stationary signals, such as vibration data, because it does not require a linear or stationary base as Fourier or wavelet transforms do. The flexibility of EMD in handling signals with time-varying frequencies and amplitudes has allowed researchers to effectively identify the different oscillatory modes of signals in various studies, namely vibration signals [30,56,59,73,74,87,93,94,101,125,126,142,160,188,192,197,198,215,225]. Moreover, EMD-based methods such as Ensemble Empirical Mode Decomposition (EEMD) [116], Complete Ensemble Empirical Mode Decomposition (CEEMD) [199], and Noise-Assisted MEMD (NAMEMD) [128] have been shown to improve the accuracy of fault diagnosis in mechanical systems by isolating and localizing faults better. However, EMD is an empirical method that lacks a solid mathematical background.Variational mode decomposition (VMD) is commonly applied in the analysis of vibration signals due to its ability to adaptively and non-recursively segregate non-stationary signals into their fundamental modes [294] and mitigate mode mixing [295], a prevalent challenge encountered in other decomposition methodologies such as Empirical Mode Decomposition (EMD). Furthermore, VMD formulates the decomposition problem as a variational optimization problem. It extracts band-limited intrinsic mode functions (IMFs) adaptively by optimizing the objective functions related to the signal’s frequency content [296].The studies referenced as [61,129,130] used this method to perform a time scale analysis of vibration signals and ultimately assess the health of rotating machinery. Other studies have used other VMD-based methods, such as adaptive variational mode decomposition in [131] and Recursive Variational Mode Extraction (RVME) [200], to diagnose faults in rolling bearings.In another study [132], VMD was coupled with other decomposition techniques, including EMD, local mean decomposition, local characteristic scale decomposition, Hilbert vibration decomposition, the EWT, and adaptive local iterative filtering, to analyze non-stationary signals from rotating machinery. Consequently, time–frequency representations (TFRs) with no interference of the cross-terms and auto-terms and a fine resolution were obtained.In the same context of decomposition techniques, the authors in [50] proposed a novel feature adaptive extraction method for time scale analysis consisting of a slope and threshold adaptive activation function with the tanh function (STAC-tanh) for diagnosing bearing faults. Additionally, in [178], the authors used Adaptive Periodic Mode Decomposition for the same purpose.
3.2.5. Other Approaches to Signal Processing
3.3. Signal Post-Processing
3.3.1. Feature Selection
3.3.2. Data Augmentation
3.3.3. Feature Fusion
3.4. Diagnosis
3.4.1. Support Vector Machines
3.4.2. Convolutional Neural Networks (CNNs)
3.4.3. Long Short-Term Memory (LSTM)
3.4.4. KNN
3.4.5. Random Forest
3.4.6. Deep Belief Networks (DBNs)
3.4.7. Other Machine Learning Algorithms
3.4.8. Comparative Research
3.5. Prognosis
3.5.1. Convolutional-Network-Based Models
3.5.2. Spatiotemporal Feature Extraction
3.5.3. ResNet and Attention Mechanisms
3.5.4. Adversarial Out-Domain Augmentation
3.5.5. Change Point Detection
3.5.6. LSTM-Based Models
3.5.7. Domain Feature Disentanglement
3.5.8. Data Transformation
3.5.9. Transformer Models
3.6. Experimental Validation
3.6.1. Reproducibility Challenges in Experimental Validation
- Machinery complexity and variability:Turbines, pumps, motors, and other rotating systems are characterized by considerable mechanical intricacy. Factors such as material degradation, fluctuations in load, misalignments, and design discrepancies can result in differing test conditions, even when the same equipment is employed. This variability poses challenges for experimental replication, as minor differences in configurations can significantly affect the vibration patterns, signal responses, and the overall efficacy of monitoring systems. The intrinsic variability in machinery operations indicates that even minor variations in the operating conditions, such as temperature, can produce divergent results.
- Operational factors and environment:The inherent variability in the dynamic settings (with temperature, humidity, vibration, and power supply fluctuations) of industrial environments often prevents researchers from achieving uniformity in their experimental setups, complicating the pursuit of reliable results. Additionally, operational parameters such as rotational speed, load, and the procedures for starting and stopping the machinery frequently vary, further complicating the design and implementation of experiments.
- Sensor parameterization:The efficacy of monitoring systems is largely influenced by the sensors employed for data acquisition, as well as the parameters for signal sampling, which are often set in the acquisition devices. Variations in the positioning, calibration, and sensitivity of sensors can produce notable discrepancies in the data gathered, even when observing the same equipment. Furthermore, the signal sampling parameters, which are often set in the acquisition devices, can lead to divergent interpretations of identical datasets. Consequently, reproducibility is further hindered if intricate parameters are not fully disclosed or comprehended by external researchers.
- Data availability and transparency:Another important aspect of experimental validation, which is data sharing, is frequently obstructed by the proprietary characteristics of industrial data and inadequate documentation of experimental parameters. Numerous researchers and companies exhibit hesitance in revealing comprehensive information regarding their equipment, primarily due to confidentiality issues. As a result, this reluctance can lead to a significant gap in access to essential experimental information or raw data, which are necessary for other researchers to accurately validate their work.
3.6.2. Reproducibility Enhancements
- Standardization of experimental protocols:Standardization of protocols related to machinery monitoring can simplify supervising and guiding experimental activities. By clearly defining the types of sensors to use, the guidelines for their placement, the methods for signal preprocessing, and the specific operating conditions, researchers can minimize discrepancies in their experimental setups. The implementation of international standards, such as ISO13373 [320], which pertains to condition monitoring and diagnostics for machinery, offers a structured approach to ensuring that experiments are conducted in a consistent manner across various laboratories and sectors. Following these established standards can lead to the improved reliability and reproducibility of experimental outcomes.
- Improved data documentation and sharing:To facilitate the replication of experiments, it is essential to meticulously document the conditions under which they are conducted and to promote open sharing of data. Researchers are encouraged to provide comprehensive details regarding the specifications of the equipment utilized, the environmental variables present, the calibration of sensors, and the particular algorithms employed in data analysis. The adoption of open access platforms and data repositories would significantly enhance the dissemination of experimental findings, allowing other researchers to replicate and expand upon previous studies. Furthermore, advocating for the implementation of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles can greatly improve the transparency and accessibility of research endeavors.
- Digital twins and simulation models:The implementation of digital twins, which are virtual representations of physical equipment, offers researchers the capability to model diverse operational scenarios in a regulated environment. By merging real-time data from existing machinery with simulation frameworks, digital twins facilitate the evaluation of monitoring strategies across a broad spectrum of conditions, eliminating the necessity for physical trials. This approach can greatly improve the consistency of findings, as the virtual setting ensures a controlled and repeatable context that reduces the impact of external factors.
3.7. Practical Industrial Implementation
4. Conclusions
- 1.
- Despite being the foundation of vibration signal analysis, the preprocessing phase was found to be significantly lacking in the datasets used in the studies we reviewed. Primarily, this issue arises because the databases already encompass the preprocessing phase yet they do not specify all parameters, including the overlap percentage, the window size, and the type of sampling window.
- 2.
- Signal processing techniques in the time domain, the frequency domain, and the time–frequency domain are widely utilized in vibration signal analysis. However, time scale domain techniques can extract non-linear information about a machinery’s health state, thereby expanding the detection results. The optimal approach to analyzing vibration signals would involve a combination of time scaling methods and time–frequency representations.
- 3.
- An emerging trend involves the representation of vibration signals as 2D images instead of traditional 1D time series signals. This innovative technique offers a new perspective on signal analysis and enables the use of image classification algorithms for fault diagnosis.
- 4.
- Incorporating a post-processing step into the construction of a diagnosis or/and prognosis model significantly improves the performance of AI algorithms. This step can rely on feature selection and dimensionality reduction, as well as data augmentation if needed.
- 5.
- In this review, SVMs, CNNs, LSTM, KNN, and random forest were found to be the top five most solicited algorithms for diagnosis. Each algorithm offers good results depending on the scalability of the data, the available resources, and the conception of its architecture.
- 6.
- Studies on the estimation of the remaining useful life (RUL) and health indexes (HIs) of rotating components generally employ traditional AI algorithms; however, the implementation of transformer-based models is being explored for this purpose.
- 7.
- The lack of experimental validation in most of these studies is notable, as they have typically tested their models’ efficiency on public databases that do not accurately represent all industrial scenarios.
- 8.
- The primary emphasis of the research in this domain lies in identifying faults in rolling bearings, with less attention given to detecting other mechanical defects, such as imbalances, misalignments, gear defects, and belt pulley defects.
5. Future Directions
- 1.
- Experimental studies that provide vibration datasets should prioritize the inclusion of preprocessing information such as sampling rates, overlap percentages, and window types and sizes. The omission of such crucial data may cause researchers to derive inaccurate results in feature extraction given that most signal processing techniques rely on these parameters.
- 2.
- The limited real-time validation and scarcity of standardized datasets also present substantial hurdles to generalizing these approaches across different machinery and conditions, advocating for an increase in collaborative efforts to create accessible and high-quality datasets.
- 3.
- Experimentation with newer machine learning architectures, such as transformers and generative adversarial networks, alongside efforts to incorporate domain knowledge directly into algorithm designs, could bolster diagnosing and prognosing capabilities further.
- 4.
- Future research efforts that investigate the feasibility and integration of these algorithms into modern industrial management systems, either by employing cutting-edge machine learning methods for real-time fault detection or by examining the potential of transfer learning for broader implementation in diverse operational contexts, are regarded as promising pathways for new academic contributions. Additionally, the explainability and interpretability of AI systems remain vital areas of focus to foster trust and broader acceptance of these technologies in real-world applications.
- 5.
- By considering the synergy between different techniques, such as combining CNNs and LSTMs, researchers can potentially address the limitations of individual methods and create more robust and effective solutions. This integrative approach not only showcases the potential for innovation in the field but also underscores the significance of collaboration and cross-pollination between diverse methodologies to advancing the accuracy of fault diagnosis.
- 6.
- The utilization of cutting-edge machine learning approaches, such as deep reinforcement learning, should be explored for prognosis modeling.
- 7.
- Utilizing EEG and ECG signal processing techniques and classification methods can yield substantial outcomes due to the shared characteristics these signals have with vibration signals.
- 8.
- It is worth noting that the vibration signals captured in bearings encapsulate information on bearing defects as well as information on other defects present in the global system. This calls for future research on the distinctions between each class of information.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
1D-DCGAN | One-Dimensional Deep Convolution Generative Adversarial Network |
AdaBoost | Adaptive Boosting |
Adam | Adaptive Moment Estimation |
Adgrad | Adaptive Gradient Algorithm |
AE | Acoustic Emissions |
AE | Autoencoder |
ANN | Artificial Neural Network |
ANFIS | Adaptive Neuro-Fuzzy Inference System |
ASCM | Acoustic-Sound-Based Condition Monitoring |
BiLSTM | Bidirectional LSTM |
BN | Batch Normalization |
BP | Backpropagation |
BQDTNN | Bayesian Quadratic Discriminant Transfer Neural Network |
CFS | Correlation-Based Feature Selection |
CNN | Convolutional Neural Network |
CSC | Cyclic Spectral Correlation |
CSCM | Cyclic Spectral Covariance Matrix |
CSCoh | Cyclic Spectral Coherence |
CS | Cuckoo Search |
CWT | Continuous Wavelet Transform |
DAC | Discriminant Analysis Classifier |
DBN | Deep Belief Network |
DFA | Detrended Fluctuation Analysis |
DFCNN | Deep Fully Convolutional Neural Network |
DNN | Deep Neural Network |
Dsrc | Source Domain |
DT | Decision Tree |
Dtar | Target Domain |
DWT | Discrete Wavelet Transform |
ECG | Electrocardiogram |
ECNN | Ensemble Convolution Neural Network |
EDAE | Ensemble Deep Autoencoder |
EDLR | Extreme Deep Learning Regression |
EEG | Electroencephalogram |
EEMD | Ensemble Empirical Mode Decomposition |
ESGMD | Enhanced Symplectic Geometry Mode Decomposition |
ELM | Extreme Learning Machine |
EMD | Empirical Mode Decomposition |
ESA | Electrical Signature Analysis |
ESM | Expectation Selection Maximization |
EVT | Empirical Wavelet Transform |
FFT | Fast Fourier Transform |
FO-SVM | Fault-Oriented Support Vector Machine |
FBE | Frequency Band Entropy |
FRBFELM | Fuzzy Logic Embedded RBF-Kernel-Based ELM |
GAMD | Generalized Adaptive Mode Decomposition |
GAN | Generative Adversarial Network |
GDA | Gaussian Discriminant Analysis |
GMM | Gaussian Mixture Model |
GNN | Graph Neural Network |
GRA | Gray Relation Analysis |
GRN | Gate Recurrent Network |
GRU | Gate Recurrent Unit |
HFD | Higuchi Fractal Dimension |
HHT | Hilbert–Huang Transform |
HI | Health Index |
HSA | Hilbert Spectral Analysis |
IEEE | Institute of Electrical and Electronics Engineers |
IoT | Internet of Things |
KExMLP | Kernel Extreme-Learning-Based Multi-Layer Perceptron |
KNN | K-Nearest Neighbors |
K-PCA | K-Principal Component Analysis |
LBP | Local Binary Pattern |
LSP | Locally Stationary Process |
LSTM | Long Short-Term Memory |
MCWD | Modified Continuous Wavelet Decomposition |
MDPI | Multidisciplinary Digital Publishing Institute |
MFCCs | Mel Frequency Cepstral Coefficients |
MFFNet | Multi-Feature Fusion Network |
MHA-LSTM | Multi-Head-Attention-Based Long Short-Term Memory |
ML | Machine Learning |
MLP | Multi-Layer Perceptron |
MRA | Multi-Resolution Analysis |
MSB | Modulation Signal Bispectrum |
MSDI | Mode Shape Damage Index |
MWSVD | Multi-Weight Singular Value Decomposition |
MVMD | Multivariate Variational Mode Decomposition |
NAMEMD | Noise-Assisted Multivariate Empirical Mode Decomposition |
NCA | Neighborhood Component Analysis |
NDT | Non-Destructive Technique |
NFN | Neuro-Fuzzy Network |
NN | Neural Network |
NUT | Normal–Uniform–Triangular |
PCA | Principal Component Analysis |
PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
PSO | Particle Swarm Optimization |
PSD | Power Spectral Density |
RBF | Radial Basis Function |
RBM | Restricted Boltzmann Machine |
RFE | Recursive Feature Elimination |
RF | Random Forest |
RMLCT | Refined Matching Liner Chirplet Transform |
RMS | Root Mean Square |
RMSProp | Root Mean Squared Propagation |
RL | Reinforcement Learning |
RNN | Recurrent Neural Network |
RUL | Remaining Useful Life |
RVME | Recursive Variational Mode Extraction |
RVM | Relevance Vector Machine |
SAE | Stacked Autoencoder |
SBFS | Sequential Backward Feature Selection |
SFFS | Sequential Forward Floating Selection |
SGD | Stochastic Gradient Descent |
SIRCNN | Stacked Inverted Residual Convolution Neural Network |
SNN | Spiking Neural Network |
SR-TT | Sparse Regularity Tensor Train |
SSA | Singular Spectrum Analysis |
STAC-tanh | Slope and Threshold Adaptive Activation Function with Tanh Function |
STFT | Short-Time Fourier Transform |
SVM | Support Vector Machine |
SVR | Support Vector Regression |
TFD | Time–Frequency Distribution |
TFR | Time–Frequency Representation |
t-SNE | T-Distributed Stochastic Neighbor Embedding |
UT | Ultrasonic Testing |
VMD | Variational Mode Decomposition |
VSA | Vibration Signal Analysis |
VS | Vibration Signal |
WNN | Wavelet Neural Network |
WPT | Wavelet Packet Transform |
WT | Wavelet Transform |
WVD | Wigner–Ville Distribution |
References
- Chen, J.; Zhou, D.; Lyu, C.; Lu, C. An approach to fault diagnosis for rotating machinery based on feature reconstruction with lcd and t-sne. Vibroengineering Procedia 2017, 11, 40–45. [Google Scholar] [CrossRef]
- Chen, J.; Zhou, D.; Lyu, C.; Lu, C. Feature reconstruction based on t-sne: An approach for fault diagnosis of rotating machinery. J. Vibroengineering 2017, 19, 5047–5060. [Google Scholar] [CrossRef]
- Chen, Y.; Zhang, T.; Zhao, W.; Luo, Z.; Lin, H. Rotating machinery fault diagnosis based on improved multiscale amplitude-aware permutation entropy and multiclass relevance vector machine. Sensors 2019, 19, 4542. [Google Scholar] [CrossRef] [PubMed]
- Guan, Z.; Liao, Z.; Li, K.; Chen, P. A precise diagnosis method of structural faults of rotating machinery based on combination of empirical mode decomposition, sample entropy, and deep belief network. Sensors 2019, 19, 591. [Google Scholar] [CrossRef] [PubMed]
- Heng, A.; Zhang, S.; Tan, A.; Mathew, J. Rotating machinery prognostics: State of the art, challenges and opportunities. Mech. Syst. Signal Process. 2009, 23, 724–739. [Google Scholar] [CrossRef]
- Kan, M.; Tan, A.; Mathew, J. A review on prognostic techniques for non-stationary and non-linear rotating systems. Mech. Syst. Signal Process. 2015, 62–63, 1–20. [Google Scholar] [CrossRef]
- Lei, Y.; Zuo, M. Fault diagnosis of rotating machinery using an improved hht based on eemd and sensitive imfs. Meas. Sci. Technol. 2009, 20, 125701. [Google Scholar] [CrossRef]
- Lei, Y.; Li, N.; Lin, J.; Wang, S. Fault diagnosis of rotating machinery based on an adaptive ensemble empirical mode decomposition. Sensors 2013, 13, 16950–16964. [Google Scholar] [CrossRef]
- Li, X.; Duan, F.; Mba, D.; Bennett, I. Multidimensional prognostics for rotating machinery: A review. Adv. Mech. Eng. 2017, 9, 168781401668500. [Google Scholar] [CrossRef]
- Lu, C.; Wang, Y.; Ragulskis, M.; Cheng, Y. Fault diagnosis for rotating machinery: A method based on image processing. PLoS ONE 2016, 11, e0164111. [Google Scholar] [CrossRef]
- Pang, B.; Tang, G.; Tian, T. Complex singular spectrum decomposition and its application to rotating machinery fault diagnosis. IEEE Access 2019, 7, 143921–143934. [Google Scholar] [CrossRef]
- Tang, M.; Liao, Y.; Luo, F.; Li, X. A novel method for fault diagnosis of rotating machinery. Entropy 2022, 24, 681. [Google Scholar] [CrossRef] [PubMed]
- Wu, B.; Feng, S.; Sun, G.; Liang, X.; Ai, C. Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network. J. Vibroengineering 2019, 21, 2106–2120. [Google Scholar] [CrossRef]
- Yan, X.; Jia, M.; Xiang, L. Compound fault diagnosis of rotating machinery based on ovmd and a 1.5-dimension envelope spectrum. Meas. Sci. Technol. 2016, 27, 075002. [Google Scholar] [CrossRef]
- Yu, X. Rotating machinery fault diagnosis under time–varying speed conditions based on adaptive identification of order structure. Processes 2024, 12, 752. [Google Scholar] [CrossRef]
- Zhang, D.; Yu, D.; Xing, L. Optimal resonance-based signal sparse decomposition and its application to fault diagnosis of rotating machinery. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 2016, 231, 4670–4683. [Google Scholar] [CrossRef]
- Samuel, P.; Pines, D. A review of vibration-based techniques for helicopter transmission diagnostics. J. Sound Vib. 2005, 282, 475–508. [Google Scholar] [CrossRef]
- Nahvi, H.; Esfahanian, M. Fault identification in rotating machinery using artificial neural networks. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 2005, 219, 141–158. [Google Scholar] [CrossRef]
- Al-Obaidi, A.; Towsyfyan, H. An experimental study on vibration signatures for detecting incipient cavitation in centrifugal pumps based on envelope spectrum analysis. J. Appl. Fluid Mech. 2019, 12, 2057–2067. [Google Scholar] [CrossRef]
- Yang, H.; Mathew, J.; Ma, L. Vibration feature extraction techniques for fault diagnosis of rotating machinery: A literature survey. In Proceedings of the Asia-Pacific Vibration Conference, Brisbane, Australia, 12–14 November 2003. [Google Scholar]
- Kumar, A.; Gandhi, C.P.; Tang, H.; Sun, W.; Xiang, J. Latest innovations in the field of condition-based maintenance of rotatory machinery: A review. Meas. Sci. Technol. 2023, 35, 022003. [Google Scholar] [CrossRef]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, n71, n71. [Google Scholar] [CrossRef] [PubMed]
- Tang, Z.; Wang, M.; Ouyang, T.; Che, F. A wind turbine bearing fault diagnosis method based on fused depth features in time–frequency domain. Energy Rep. 2022, 8, 12727–12739. [Google Scholar] [CrossRef]
- Zhu, J.; Hu, T.; Jiang, B.; Yang, X. Intelligent bearing fault diagnosis using PCA–DBN framework. Neural Comput. Appl. 2020, 32, 10773–10781. [Google Scholar] [CrossRef]
- Liu, R.; Yang, B.; Hauptmann, A.G. Simultaneous Bearing Fault Recognition and Remaining Useful Life Prediction Using Joint-Loss Convolutional Neural Network. IEEE Trans. Ind. Inform. 2020, 16, 87–96. [Google Scholar] [CrossRef]
- Goyal, D.; Choudhary, A.; Pabla, B.S.; Dhami, S.S. Support vector machines based non-contact fault diagnosis system for bearings. J. Intell. Manuf. 2019, 31, 1275–1289. [Google Scholar] [CrossRef]
- Savadkoohi, M.; Oladunni, T.; Thompson, L. A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal. Biocybern. Biomed. Eng. 2020, 40, 1328–1341. [Google Scholar] [CrossRef]
- Khosla, A.; Khandnor, P.; Chand, T. A comparative analysis of signal processing and classification methods for different applications based on EEG signals. Biocybern. Biomed. Eng. 2020, 40, 649–690. [Google Scholar] [CrossRef]
- Ganapathy, N.; Veeranki, Y.R.; Swaminathan, R. Convolutional neural network based emotion classification using electrodermal activity signals and time-frequency features. Expert Syst. Appl. 2020, 159, 113571. [Google Scholar] [CrossRef]
- Chen, X.; Zhang, B.; Gao, D. Bearing fault diagnosis base on multi-scale CNN and LSTM model. J. Intell. Manuf. 2021, 32, 971–987. [Google Scholar] [CrossRef]
- Zhang, Y.; Xing, K.; Bai, R.; Sun, D.; Meng, Z. An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image. Measurement 2020, 157, 107667. [Google Scholar] [CrossRef]
- Anderson, R.; Sandsten, M. Time-frequency feature extraction for classification of episodic memory. EURASIP J. Adv. Signal Process. 2020, 2020, 19. [Google Scholar] [CrossRef]
- Zhang, J.; Sun, Y.; Guo, L.; Gao, H.; Hong, X.; Song, H. A new bearing fault diagnosis method based on modified convolutional neural networks. Chin. J. Aeronaut. 2020, 33, 439–447. [Google Scholar] [CrossRef]
- Wu, S.; Jing, X.-Y.; Zhang, Q.; Wu, F.; Zhao, H.; Dong, Y. Prediction Consistency Guided Convolutional Neural Networks for Cross-Domain Bearing Fault Diagnosis. IEEE Access 2020, 8, 120089–120103. [Google Scholar] [CrossRef]
- Castellani, F.; Garibaldi, L.; Daga, A.P.; Astolfi, D.; Natili, F. Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements. Energies 2020, 13, 1474. [Google Scholar] [CrossRef]
- Mubaraali, L.; Kuppuswamy, N.; Muthukumar, R. Intelligent fault diagnosis in microprocessor systems for vibration analysis in roller bearings in whirlpool turbine generators real time processor applications. Microprocess. Microsystems 2020, 76, 103079. [Google Scholar] [CrossRef]
- Junior, R.F.R.; de Almeida, F.A.; Gomes, G.F. Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks. Neural Comput. Appl. 2020, 32, 15171–15189. [Google Scholar] [CrossRef]
- AlShorman, O.; Masadeh, M.; Alkahtani, F.; AlShorman, A. A Review of Condition Monitoring and Fault Diagnosis and Detection of Rotating Machinery Based on Image Aspects. In Proceedings of the 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), Sakheer, Bahrain, 26–27 October 2020; pp. 1–5. [Google Scholar] [CrossRef]
- Gougam, F.; Chemseddine, R.; Benazzouz, D.; Benaggoune, K.; Zerhouni, N. Fault prognostics of rolling element bearing based on feature extraction and supervised machine learning: Application to shaft wind turbine gearbox using vibration signal. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 2020, 235, 5186–5197. [Google Scholar] [CrossRef]
- Shifat, T.A.; Hur, J.-W. EEMD assisted supervised learning for the fault diagnosis of BLDC motor using vibration signal. J. Mech. Sci. Technol. 2020, 34, 3981–3990. [Google Scholar] [CrossRef]
- Tayyab, S.M.; Asghar, E.; Pennacchi, P.; Chatterton, S. Intelligent fault diagnosis of rotating machine elements using machine learning through optimal features extraction and selection. Procedia Manuf. 2020, 51, 266–273. [Google Scholar] [CrossRef]
- Feng, K.; Smith, W.A.; Borghesani, P.; Randall, R.B.; Peng, Z. Use of cyclostationary properties of vibration signals to identify gear wear mechanisms and track wear evolution. Mech. Syst. Signal Process. 2021, 150, 107258. [Google Scholar] [CrossRef]
- Chinara, S. Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal. J. Neurosci. Methods 2021, 347, 108927. [Google Scholar] [CrossRef]
- Movahed, R.A.; Jahromi, G.P.; Shahyad, S.; Meftahi, G.H. A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis. J. Neurosci. Methods 2021, 358, 109209. [Google Scholar] [CrossRef] [PubMed]
- Huang, J.; Chen, B.; Li, Y.; Sun, W. Fractal geometry of wavelet decomposition in mechanical signature analysis. Measurement 2021, 173, 108571. [Google Scholar] [CrossRef]
- Kafeel, A.; Aziz, S.; Awais, M.; Khan, M.A.; Afaq, K.; Idris, S.A.; Alshazly, H.; Mostafa, S.M. An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis. Sensors 2021, 21, 7587. [Google Scholar] [CrossRef]
- Chen, Z.; Guo, L.; Gao, H.; Yu, Y.; Wu, W.; You, Z.; Dong, X. A fault pulse extraction and feature enhancement method for bearing fault diagnosis. Measurement 2021, 182, 109718. [Google Scholar] [CrossRef]
- Han, B.; Zhou, Y.; Yu, G. Second-order synchroextracting wavelet transform for nonstationary signal analysis of rotating machinery. Signal Process. 2021, 186, 108123. [Google Scholar] [CrossRef]
- Zhang, Q.; Ji, H.; Jin, Y. Cyclostationary Signals Analysis Methods Based on High-Dimensional Space Transformation Under Impulsive Noise. IEEE Signal Process. Lett. 2021, 28, 1724–1728. [Google Scholar] [CrossRef]
- Zhang, T.; Liu, S.; Wei, Y.; Zhang, H. A novel feature adaptive extraction method based on deep learning for bearing fault diagnosis. Measurement 2021, 185, 110030. [Google Scholar] [CrossRef]
- Wang, X.; Mao, D.; Li, X. Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network. Measurement 2021, 173, 108518. [Google Scholar] [CrossRef]
- Zhang, X.; Rane, K.P.; Kakaravada, I.; Shabaz, M. Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology. Nonlinear Eng. 2021, 10, 245–254. [Google Scholar] [CrossRef]
- Lin, S.-L. Application of Machine Learning to a Medium Gaussian Support Vector Machine in the Diagnosis of Motor Bearing Faults. Electronics 2021, 10, 2266. [Google Scholar] [CrossRef]
- AbdulBary, M.; Embaby, A.; Gomaa, F. Fault Diagnosis in Rotating System Based on Vibration Analysis. ERJ Eng. Res. J. 2021, 44, 285–294. [Google Scholar] [CrossRef]
- Peng, Y.; Qiao, W.; Cheng, F.; Qu, L. Wind Turbine Drivetrain Gearbox Fault Diagnosis Using Information Fusion on Vibration and Current Signals. IEEE Trans. Instrum. Meas. 2021, 70, 1–11. [Google Scholar] [CrossRef]
- Ma, L.; Jiang, H.; Ma, T.; Zhang, X.; Shen, Y.; Xia, L. Fault Prediction of Rolling Element Bearings Using the Optimized MCKD–LSTM Model. Machines 2022, 10, 342. [Google Scholar] [CrossRef]
- Zuo, L.; Xu, F.; Zhang, C.; Xiahou, T.; Liu, Y. A multi-layer spiking neural network-based approach to bearing fault diagnosis. Reliab. Eng. Syst. Saf. 2022, 225, 108561. [Google Scholar] [CrossRef]
- Shi, J.; Hua, Z.; Dumond, P.; Zhu, Z.; Huang, W.; Shen, C. Refined matching linear chirplet transform for exhibiting time-frequency features of nonstationary vibration and acoustic signals. Measurement 2022, 187, 110298. [Google Scholar] [CrossRef]
- Pan, T.; Xu, Y.; He, Y. A novel approach for rolling bearing fault diagnosis based on ensemble empirical mode decomposition and weighted permutation entropy. J. Vib. Control 2021, 27, 2613–2622. [Google Scholar]
- Harishvijey, A.; Raja, J.B. Automated technique for EEG signal processing to detect seizure with optimized Variable Gaussian Filter and Fuzzy RBFELM classifier. Biomed. Signal Process. Control 2022, 74, 103450. [Google Scholar] [CrossRef]
- Zhou, J.; Xiao, M.; Niu, Y.; Ji, G. Rolling Bearing Fault Diagnosis Based on WGWOA-VMD-SVM. Sensors 2022, 22, 6281. [Google Scholar] [CrossRef]
- López, C.; Naranjo, Á.; Lu, S.; Moore, K.J. Hidden Markov Model based Stochastic Resonance and its Application to Bearing Fault Diagnosis. J. Sound Vib. 2022, 528, 116890. [Google Scholar] [CrossRef]
- Lin, R.; Yu, Y.; Wang, H.; Che, C.; Ni, X. Remaining useful life prediction in prognostics using multi-scale sequence and Long Short-Term Memory network. J. Comput. Sci. 2022, 57, 101508. [Google Scholar] [CrossRef]
- Nirwan, N.W.; Ramani, H.B. Condition monitoring and fault detection in roller bearing used in rolling mill by acoustic emission and vibration analysis. Mater. Today Proc. 2022, 51, 344–354. [Google Scholar] [CrossRef]
- Hosseinpour-Zarnaq, M.; Omid, M.; Biabani-Aghdam, E. Fault diagnosis of tractor auxiliary gearbox using vibration analysis and random forest classifier. Inf. Process. Agric. 2022, 9, 60–67. [Google Scholar] [CrossRef]
- Lupea, I.; Lupea, M. Fault detection on a rotating test rig based on vibration analysis and machine learning. Proc. Rom. Acad. Ser. A Math. Physics, Tech. Sci. Inf. Sci. 2022, 23, 153–164. [Google Scholar]
- Meyer, A. Vibration Fault Diagnosis in Wind Turbines Based on Automated Feature Learning. Energies 2022, 15, 1514. [Google Scholar] [CrossRef]
- Gupta, M.; Wadhvani, R.; Rasool, A. A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network. Knowl.-Based Syst. 2023, 259, 110070. [Google Scholar] [CrossRef]
- Zhang, J.; Squicciarini, G.; Thompson, D.J.; Sun, W.; Zhang, X. A hybrid time and frequency domain beamforming method for application to source localisation on high-speed trains. Mech. Syst. Signal Process. 2023, 200, 110494. [Google Scholar] [CrossRef]
- Morales, S.; Bowers, M.E. Time-frequency analysis methods and their application in developmental EEG data. Dev. Cogn. Neurosci. 2022, 54, 101067. [Google Scholar] [CrossRef]
- Liu, J.; Tan, Y.; Liu, Z.; Zhao, W.; Zhang, Y. Enhanced time-frequency method for characterizing transient modulation of rubbing signals in rotor systems. Mech. Syst. Signal Process. 2023, 198, 109507. [Google Scholar]
- Guo, J.; He, Q.; Zhen, D.; Gu, F.; Ball, A.D. Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis. Reliab. Eng. Syst. Saf. 2023, 230, 108969. [Google Scholar] [CrossRef]
- Zhou, K.; Tang, J. A wavelet neural network informed by time-domain signal preprocessing for bearing remaining useful life prediction. Appl. Math. Model. 2023, 122, 220–241. [Google Scholar] [CrossRef]
- Zhao, K.; Xiao, J.; Li, C.; Xu, Z.; Yue, M. Fault diagnosis of rolling bearing using CNN and PCA fractal based feature extraction. Measurement 2023, 223, 113754. [Google Scholar] [CrossRef]
- Li, F.; Wang, L.; Wang, D.; Wu, J.; Zhao, H. An adaptive multiscale fully convolutional network for bearing fault diagnosis under noisy environments. Measurement 2023, 216, 112993. [Google Scholar] [CrossRef]
- Janapati, R.; Dalal, V.; Sengupta, R. Advances in modern EEG-BCI signal processing: A review. Mater. Today Proc. 2023, 80, 2563–2566. [Google Scholar] [CrossRef]
- Jafari, M.; Shoeibi, A.; Khodatars, M.; Bagherzadeh, S.; Shalbaf, A.; García, D.L.; Gorriz, J.M.; Acharya, U.R. Emotion recognition in EEG signals using deep learning methods: A review. Comput. Biol. Med. 2023, 165, 107450. [Google Scholar] [CrossRef]
- Zhao, J.; Du, J.; Zhu, B.; Luo, X.; Tao, X. Indirect tire pressure monitoring method based on the fusion of time and frequency domain analysis. Measurement 2023, 220, 113282. [Google Scholar] [CrossRef]
- Li, Y.; Wang, J.; Feng, D.; Jiang, M.; Peng, C.; Geng, X.; Zhang, F. Bearing fault diagnosis method based on maximum noise ratio kurtosis product deconvolution with noise conditions. Measurement 2023, 221, 113542. [Google Scholar] [CrossRef]
- Hong, J.; Yang, H.; Ma, F. Multi-forward-step battery voltage prediction for real-world electric vehicles using gated recurrent units. J. Energy Storage 2023, 73, 109056. [Google Scholar] [CrossRef]
- Peng, B.; Zhang, Y.; Wang, M.; Chen, J.; Gao, D. T-A-MFFNet: Multi-feature fusion network for EEG analysis and driving fatigue detection based on time domain network and attention network. Comput. Biol. Chem. 2023, 104, 107863. [Google Scholar] [CrossRef]
- Zali-Vargahan, B.; Charmin, A.; Kalbkhani, H.; Barghandan, S. Deep time-frequency features and semi-supervised dimension reduction for subject-independent emotion recognition from multi-channel EEG signals. Biomed. Signal Process. Control 2023, 85, 104806. [Google Scholar] [CrossRef]
- Hadi, R.H.; Hady, H.N.; Hasan, A.M.; Al-Jodah, A.; Humaidi, A.J. Improved Fault Classification for Predictive Maintenance in Industrial IoT Based on AutoML: A Case Study of Ball-Bearing Faults. Processes 2023, 11, 1507. [Google Scholar] [CrossRef]
- Emmanuel, T.; Mpoeleng, D.; Maupong, T. Power plant induced-draft fan fault prediction using machine learning stacking ensemble. J. Eng. Res. 2023, 12, 82–90. [Google Scholar] [CrossRef]
- Sun, J.; Zhang, X.; Wang, J. Lightweight bidirectional long short-term memory based on automated model pruning with application to bearing remaining useful life prediction. Eng. Appl. Artif. Intell. 2023, 118, 105662. [Google Scholar] [CrossRef]
- Zhou, J.; Yang, J.; Qin, Y. A systematic overview of health indicator construction methods for rotating machinery. Eng. Appl. Artif. Intell. 2024, 138, 109356. [Google Scholar] [CrossRef]
- Fang, K.; Zhang, H.; Qi, H.; Dai, Y. Comparison of EMD and EEMD in rolling bearing fault signal analysis. In Proceedings of the 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, USA, 14–17 May 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Harpale, V.K.; Bairagi, V.K. Time and frequency domain analysis of EEG signals for seizure detection: A review. In Proceedings of the 2016 International Conference on Microelectronics, Computing and Communications (MicroCom), Durgapur, India, 23–25 January 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 1–6. [Google Scholar] [CrossRef]
- Keshtan, M.N.; Khajavi, M.N. Bearings Fault Diagnosis Using Vibrational Signal Analysis by EMD Method. Res. Nondestruct. Eval. 2016, 27, 155–174. [Google Scholar] [CrossRef]
- Nguyen, H.; Kim, C.-H.; Kim, J.-M. Effective Prediction of Bearing Fault Degradation under Different Crack Sizes Using a Deep Neural Network. Appl. Sci. 2018, 8, 2332. [Google Scholar] [CrossRef]
- Zhang, R.; Gu, F.; Mansaf, H.; Wang, T.; Ball, A.D. Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis. Mech. Syst. Signal Process. 2017, 94, 202–213. [Google Scholar] [CrossRef]
- Gu, Y.H.; Bollen, M.H.J. Time-frequency and time-scale domain analysis of voltage disturbances. IEEE Trans. Power Deliv. 2000, 15, 1279–1284. [Google Scholar] [CrossRef]
- Feldman, M. Analytical basics of the EMD: Two harmonics decomposition. Mech. Syst. Signal Process. 2009, 23, 2059–2071. [Google Scholar] [CrossRef]
- Lobato, T.H.G.; Silva, R.R.D.; Costa, E.S.D.; Mesquita, A.L.A. An Integrated Approach to Rotating Machinery Fault Diagnosis Using, EEMD, SVM, and Augmented Data. J. Vib. Eng. Technol. 2020, 8, 403–408. [Google Scholar] [CrossRef]
- Yu, K.; Lin, T.R.; Tan, J. A bearing fault and severity diagnostic technique using adaptive deep belief networks and Dempster–Shafer theory. Struct. Health Monit. 2020, 19, 240–261. [Google Scholar] [CrossRef]
- Lin, L.; Hongbing, J. Signal feature extraction based on an improved EMD method. Measurement 2009, 42, 796–803. [Google Scholar] [CrossRef]
- Xue, Y.; Dou, D.; Yang, J. Multi-fault diagnosis of rotating machinery based on deep convolution neural network and support vector machine. Measurement 2020, 156, 107571. [Google Scholar] [CrossRef]
- Li, H.; Liu, T.; Wu, X.; Chen, Q. A Bearing Fault Diagnosis Method Based on Enhanced Singular Value Decomposition. IEEE Trans. Ind. Inform. 2021, 17, 3220–3230. [Google Scholar] [CrossRef]
- Chen, X.; Feng, Z. Induction motor stator current analysis for planetary gearbox fault diagnosis under time-varying speed conditions. Mech. Syst. Signal Process. 2020, 140, 106691. [Google Scholar] [CrossRef]
- Merizalde, Y.; Hernández-Callejo, L.; Duque-Perez, O.; López-Meraz, R.A. Fault Detection of Wind Turbine Induction Generators through Current Signals and Various Signal Processing Techniques. Appl. Sci. 2020, 10, 7389. [Google Scholar] [CrossRef]
- Sun, H.; Si, Q.; Chen, N.; Yuan, S. HHT-based feature extraction of pump operation instability under cavitation conditions through motor current signal analysis. Mech. Syst. Signal Process. 2020, 139, 106613. [Google Scholar] [CrossRef]
- Rahman, M.M.; Fattah, S.A. An efficient feature extraction scheme for classification of mental tasks based on inter-channel correlation in wavelet domain utilizing EEG signal. Biomed. Signal Process. Control 2020, 61, 102033. [Google Scholar] [CrossRef]
- Have, B.T.; Moonen, N.; Leferink, F. Time Domain Analysis of Current Transducer Responses Using Impulsive Signals. IEEE Lett. Electromagn. Compat. Pract. Appl. 2021, 3, 19–23. [Google Scholar] [CrossRef]
- Li, D.; Cai, Z.; Qin, B.; Deng, L. Signal frequency domain analysis and sensor fault diagnosis based on artificial intelligence. Comput. Commun. 2020, 160, 71–80. [Google Scholar] [CrossRef]
- Pandiyan, V.; Drissi-Daoudi, R.; Shevchik, S.; Masinelli, G.; Logé, R.; Wasmer, K. Analysis of time, frequency and time-frequency domain features from acoustic emissions during Laser Powder-Bed fusion process. Procedia CIRP 2020, 94, 392–397. [Google Scholar] [CrossRef]
- Tang, S.; Yuan, S.; Zhu, Y. Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery. IEEE Access 2020, 8, 149487–149496. [Google Scholar] [CrossRef]
- Wu, Z.; Jiang, H.; Zhao, K.; Li, X. An adaptive deep transfer learning method for bearing fault diagnosis. Measurement 2020, 151, 107227. [Google Scholar] [CrossRef]
- Yao, D.; Liu, H.; Yang, J.; Li, X. A lightweight neural network with strong robustness for bearing fault diagnosis. Measurement 2020, 159, 107756. [Google Scholar] [CrossRef]
- Lim, C.; Kim, S.; Seo, Y.-H.; Choi, J.-H. Feature extraction for bearing prognostics using weighted correlation of fault frequencies over cycles. Struct. Health Monit. 2020, 19, 1808–1820. [Google Scholar] [CrossRef]
- Wang, T.; Liu, Z.; Mrad, N. A Probabilistic Framework for Remaining Useful Life Prediction of Bearings. IEEE Trans. Instrum. Meas. 2021, 70, 1–12. [Google Scholar] [CrossRef]
- Soualhi, A.; Medjaher, K.; Celrc, G.; Razik, H. Prediction of bearing failures by the analysis of the time series. Mech. Syst. Signal Process. 2020, 139, 106607. [Google Scholar] [CrossRef]
- Shifat, T.A.; Hur, J.W. An Effective Stator Fault Diagnosis Framework of BLDC Motor Based on Vibration and Current Signals. IEEE Access 2020, 8, 106968–106981. [Google Scholar] [CrossRef]
- Kumar, P.; Das, A.K.; Prachita; Halder, S. Time-domain HRV Analysis of ECG Signal under Different Body Postures. Procedia Comput. Sci. 2020, 167, 1705–1710. [Google Scholar] [CrossRef]
- Chen, Z.; Mauricio, A.; Li, W.; Gryllias, K. A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks. Mech. Syst. Signal Process. 2020, 140, 106683. [Google Scholar] [CrossRef]
- Chen, H.-Y.; Lee, C.-H. Vibration Signals Analysis by Explainable Artificial Intelligence (XAI) Approach: Application on Bearing Faults Diagnosis. IEEE Access 2020, 8, 134246–134256. [Google Scholar] [CrossRef]
- Pham, M.T.; Kim, J.-M.; Kim, C.H. Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems. Sensors 2020, 20, 6886. [Google Scholar] [CrossRef] [PubMed]
- Tang, Y.; Tang, B.; Deng, Y.; Zhang, S.; He, H. An Improved Multiscale Weighted Permutation Entropy for Fault Diagnosis of Rolling Bearings. IEEE Trans. Instrum. Meas. 2020, 69, 4535–4544. [Google Scholar]
- Al-Qerem, A.; Kharbat, F.; Nashwan, S.; Ashraf, S.; Blaou, K. General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution. Int. J. Distrib. Sens. Netw. 2020, 16, 1550147720911009. [Google Scholar] [CrossRef]
- Xu, L.; Zhang, S.; Yuan, Z.; Xiao, Y. A novel deep learning model for intelligent fault diagnosis of rolling element bearings combining convolution neural network and long short-term memory. Measurement 2020, 164, 108046. [Google Scholar] [CrossRef]
- Xie, C.; Chen, G.; Lu, W.; Dong, Z. Intelligent fault diagnosis of rolling bearing based on feature enhancement and improved convolutional neural network. J. Manuf. Process. 2021, 68, 1045–1055. [Google Scholar] [CrossRef]
- Zhen, Z.; Wang, Z.; Liang, X.; Wang, W.; Zhang, Y.; Yang, S. An enhanced method for rolling bearing fault diagnosis based on variational mode decomposition and convolutional neural networks. Measurement 2020, 149, 106963. [Google Scholar] [CrossRef]
- Kruczek, P.; Zimroz, R.; Wyłomańska, A. How to detect the cyclostationarity in heavy-tailed distributed signals. Signal Process. 2020, 172, 107514. [Google Scholar] [CrossRef]
- Miao, H.; Zhang, F.; Tao, R. Novel Second-Order Statistics of the Chirp Cyclostationary Signals. IEEE Signal Process. Lett. 2020, 27, 910–914. [Google Scholar] [CrossRef]
- Tang, S.; Yuan, S.; Zhu, Y. Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery. Processes 2020, 8, 1217. [Google Scholar] [CrossRef]
- Altuve, M.; Suárez, L.; Ardila, J. Fundamental heart sounds analysis using improved complete ensemble EMD with adaptive noise. Biocybern. Biomed. Eng. 2020, 40, 426–439. [Google Scholar] [CrossRef]
- Meng, Z.; Li, J.; Yin, N.; Pan, Z. Remaining useful life prediction of rolling bearing using fractal theory. Measurement 2020, 156, 107572. [Google Scholar] [CrossRef]
- Gangsar, P.; Tiwari, R. Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mech. Syst. Signal Process. 2020, 144, 106908. [Google Scholar] [CrossRef]
- Rafiq, H.J.; Rashed, G.I.; Shafik, M.B. Application of multivariate signal analysis in vibration-based condition monitoring of wind turbine gearbox. Int. Trans. Electr. Energy Syst. 2021, 31, e12874. [Google Scholar] [CrossRef]
- Hu, Q.; Si, X.; Zhang, Q.; Qin, A. A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests. Mech. Syst. Signal Process. 2020, 139, 106609. [Google Scholar] [CrossRef]
- Li, H.; Liu, T.; Wu, X.; Chen, Q. An optimized VMD method and its applications in bearing fault diagnosis. Measurement 2020, 166, 108185. [Google Scholar] [CrossRef]
- Jiang, X.; Wang, J.; Shen, C.; Shi, J.; Huang, W.; Zhu, Z.; Wang, Q. An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis. Struct. Health Monit. 2021, 20, 2708–2725. [Google Scholar] [CrossRef]
- Feng, Z.; Yu, X.; Zhang, D.; Liang, M. Generalized adaptive mode decomposition for nonstationary signal analysis of rotating machinery: Principle and applications. Mech. Syst. Signal Process. 2020, 136, 106530. [Google Scholar] [CrossRef]
- Kaplan, K.; Kaya, Y.; Kuncan, M.; Minaz, M.R.; Ertunc, H.M. An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis. Appl. Soft Comput. 2020, 87, 106019. [Google Scholar] [CrossRef]
- Toma, R.N.; Prosvirin, A.E.; Kim, J.-M. Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers. Sensors 2020, 20, 1884. [Google Scholar] [CrossRef]
- Hosseini, E.; Mirzaei, A. An Improved Method for Diagnosis of Induction Motor Load Mechanical Unbalance Fault Using Current Signal Analysis. Russ. Electr. Eng. 2020, 91, 217–224. [Google Scholar] [CrossRef]
- Kumar, A.; Gandhi, C.P.; Zhou, Y.; Kumar, R.; Xiang, J. Latest developments in gear defect diagnosis and prognosis: A review. Measurement 2020, 158, 107735. [Google Scholar] [CrossRef]
- Jain, P.; Bhosle, S. Analysis on Vibration Signal Analysis Techniques used in Diagnosis of Faults in Rotating Machinery. Int. J. Mech. Prod. 2020, 10, 3377–3396. [Google Scholar]
- Pinedo-Sánchez, L.A.; Mercado-Ravell, D.A.; Carballo-Monsivais, C.A. Vibration analysis in bearings for failure prevention using CNN. J. Braz. Soc. Mech. Sci. Eng. 2020, 42, 628. [Google Scholar] [CrossRef]
- Gundewar, S.K.; Kane, P.V. Condition Monitoring and Fault Diagnosis of Induction Motor. J. Vib. Eng. Technol. 2020, 9, 643–674. [Google Scholar] [CrossRef]
- Kolar, D.; Lisjak, D.; Pająk, M.; Pavković, D. Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input. Sensors 2020, 20, 4017. [Google Scholar] [CrossRef]
- Xin, Y.; Li, S.; Wang, J.; An, Z.; Zhang, W. Intelligent fault diagnosis method for rotating machinery based on vibration signal analysis and hybrid multi-object deep CNN. IET Sci. Meas. Technol. 2020, 14, 407–415. [Google Scholar] [CrossRef]
- Goyal, J.; Khandnor, P.; Aseri, T.C. A Hybrid Approach for Parkinson’s Disease diagnosis with Resonance and Time-Frequency based features from Speech signals. Expert Syst. Appl. 2021, 182, 115283. [Google Scholar] [CrossRef]
- Pancaldi, F.; Rubini, R.; Cocconcelli, M. Time-varying metrics of cyclostationarity for bearing diagnostic. Mech. Syst. Signal Process. 2021, 151, 107329. [Google Scholar] [CrossRef]
- Zhang, X.; Zhao, B.; Lin, Y. Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review. IEEE Access 2021, 9, 155598–155608. [Google Scholar] [CrossRef]
- Mao, W.; Feng, W.; Liu, Y.; Zhang, D.; Liang, X. A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis. Mech. Syst. Signal Process. 2021, 150, 107233. [Google Scholar] [CrossRef]
- Altaf, M.; Akram, T.; Khan, M.A.; Iqbal, M.; Ch, M.M.I.; Hsu, C.-H. A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals. Sensors 2022, 22, 2012. [Google Scholar] [CrossRef] [PubMed]
- Jin, C.; Chen, X. An end-to-end framework combining time–frequency expert knowledge and modified transformer networks for vibration signal classification. Expert Syst. Appl. 2021, 171, 114570. [Google Scholar] [CrossRef]
- Kim, Y.; Ha, J.M.; Na, K.; Park, J.; Youn, B.D. Cepstrum-assisted empirical wavelet transform (CEWT)-based improved demodulation analysis for fault diagnostics of planetary gearboxes. Measurement 2021, 183, 109796. [Google Scholar] [CrossRef]
- Zhang, B.; Miao, Y.; Lin, J.; Yi, Y. Adaptive maximum second-order cyclostationarity blind deconvolution and its application for locomotive bearing fault diagnosis. Mech. Syst. Signal Process. 2021, 158, 107736. [Google Scholar] [CrossRef]
- Silik, A.; Noori, M.; Altabey, W.A.; Ghiasi, R.; Wu, Z. Comparative Analysis of Wavelet Transform for Time-Frequency Analysis and Transient Localization in Structural Health Monitoring. Struct. Durab. Health Monit. 2021, 15, 1–22. [Google Scholar] [CrossRef]
- Gupta, N.; K, S.; Datta, S.S. Wavelet based real-time monitoring of electrical signals in Distributed Generation (DG) integrated system. Eng. Sci. Technol. Int. J. 2021, 24, 218–228. [Google Scholar] [CrossRef]
- Akan, A.; Ozbek, O.L. Comparison of FFT and Gabor Transform for Discrimination of Heart Sounds. In Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, 17–18 January 2006; pp. 450–453. [Google Scholar] [CrossRef]
- Wu, Y.; Wu, W. Analysis of wavelet decomposition properties of wind turbine signal. Energy Rep. 2021, 7 (Suppl. 7), 873–879. [Google Scholar] [CrossRef]
- Ahmad, S.; Saeed, S.; Saeed, Z.; Ahmad, N. A Hybrid Deep Learning Model for the Efficient Diagnosis of Bearing Faults in Rotating Machines. Appl. Sci. 2021, 11, 5089. [Google Scholar] [CrossRef]
- Zuo, J.; Wang, G.; Zhao, D.; Jiang, M. Improved EWT-based fault diagnosis of rolling bearings via recursive singular spectrum analysis. Meas. Sci. Technol. 2023, 34, 035013. [Google Scholar]
- Yuan, H.; Sun, W.; Zhang, W.; Zhang, H. A Novel Bearing Fault Diagnosis Method Based on Improved Frequency Slice Wavelet Transform. IEEE Access 2020, 8, 192960–192972. [Google Scholar]
- Zhu, H.; He, Z.; Wei, J.; Wang, J.; Zhou, H. Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion. Sensors 2021, 21, 2524. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Zhang, C.; Wang, Z.; Zhou, Z.; Zhang, X. An Enhanced Bearing Fault Diagnosis Method Based on Optimized Variational Mode Decomposition and Hybrid Deep Learning Network. Sensors 2021, 21, 1558. [Google Scholar] [CrossRef]
- Li, Y.; Jiao, S.; Gao, X. A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy. Def. Technol. 2021, 17, 1625–1635. [Google Scholar] [CrossRef]
- Meng, D.; Wang, H.; Yang, S.; Lv, Z.; Hu, Z.; Wang, Z. Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction. Comput. Model. Eng. Sci. 2022, 130, 543–558. [Google Scholar] [CrossRef]
- Han, T.; Zhang, L.; Yin, Z.; Tan, A.C.C. Rolling bearing fault diagnosis with combined convolutional neural networks and support vector machine. Measurement 2021, 177, 109022. [Google Scholar] [CrossRef]
- Chen, J.; Huang, R.; Zhao, K.; Wang, W.; Liu, L.; Li, W. Multiscale Convolutional Neural Network with Feature Alignment for Bearing Fault Diagnosis. IEEE Trans. Instrum. Meas. 2021, 70, 1–10. [Google Scholar] [CrossRef]
- Han, T.; Li, Y.-F.; Qian, M. A Hybrid Generalization Network for Intelligent Fault Diagnosis of Rotating Machinery Under Unseen Working Conditions. IEEE Trans. Instrum. Meas. 2021, 70, 1–11. [Google Scholar] [CrossRef]
- Zhang, Y.; Zhou, T.; Huang, X.; Cao, L.; Zhou, Q. Fault diagnosis of rotating machinery based on recurrent neural networks. Measurement 2021, 171, 108774. [Google Scholar] [CrossRef]
- Jacob, J.E.; Chandrasekharan, S.; Nair, G.K.; Cherian, A.; Iype, T. Effect of combining features generated through non-linear analysis and wavelet transform of EEG signals for the diagnosis of encephalopathy. Neurosci. Lett. 2021, 765, 136269. [Google Scholar] [CrossRef]
- Beretta, M.; Vidal, Y.; Sepulveda, J.; Porro, O.; Cusidó, J. Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis. Appl. Sci. 2021, 11, 7523. [Google Scholar] [CrossRef]
- Han, T.; Pang, J.; Tan, A.C.C. Remaining useful life prediction of bearing based on stacked autoencoder and recurrent neural network. J. Manuf. Syst. 2021, 61, 576–591. [Google Scholar] [CrossRef]
- Jain, P.H.; Bhosle, S.P. A Review on Vibration Signal Analysis Techniques Used for Detection of Rolling Element Bearing Defects. Int. J. Mech. Eng. 2021, 8, 14–29. [Google Scholar] [CrossRef]
- Wang, X.; Wang, T.; Ming, A.; Zhang, W.; Li, A.; Chu, F. Spatiotemporal non-negative projected convolutional network with bidirectional NMF and 3DCNN for remaining useful life estimation of bearings. Neurocomputing 2021, 450, 294–310. [Google Scholar] [CrossRef]
- Yu, W.; Pi, D.; Xie, L.; Luo, Y. Multiscale attentional residual neural network framework for remaining useful life prediction of bearings. Measurement 2021, 177, 109310. [Google Scholar] [CrossRef]
- Cao, Y.; Ding, Y.; Jia, M.; Tian, R. A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings. Reliab. Eng. Syst. Saf. 2021, 215, 107813. [Google Scholar] [CrossRef]
- Ewert, P.; Orlowska-Kowalska, T.; Jankowska, K. Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks. Energies 2021, 14, 712. [Google Scholar] [CrossRef]
- Xu, J.; Ding, X.; Gong, Y.; Wu, N.; Yan, H. Rotor imbalance detection and quantification in wind turbines via vibration analysis. Wind. Eng. 2021, 46, 3–11. [Google Scholar] [CrossRef]
- Yang, B.; Cai, A.; Lin, W. Analysis of early fault vibration detection and analysis of offshore wind power transmission based on deep neural network. Connect. Sci. 2022, 34, 1005–1017. [Google Scholar] [CrossRef]
- Mi, Z.; Wang, T.; Sun, Z.; Kumar, R. Vibration signal diagnosis and analysis of rotating machine by utilizing cloud computing. Nonlinear Eng. 2021, 10, 404–413. [Google Scholar] [CrossRef]
- Liu, W.Y.; Gu, H.; Gao, Q.W.; Zhang, Y. A review on wind turbines gearbox fault diagnosis methods. J. Vibroengineering 2021, 23, 26–43. [Google Scholar] [CrossRef]
- Jia, Z.; Sharma, A. Review on engine vibration fault analysis based on data mining. J. Vibroengineering 2021, 23, 1433–1445. [Google Scholar] [CrossRef]
- Cheng, J.; Yang, Y.; Li, X.; Cheng, J. Adaptive periodic mode decomposition and its application in rolling bearing fault diagnosis. Mech. Syst. Signal Process. 2021, 161, 107943. [Google Scholar] [CrossRef]
- Santharaguru, N.; Abdullah, S.; Chin, C.H.; Singh, S.S.K. Failure behaviour of strain and acceleration signals using various fatigue life models in time and frequency domains. Eng. Fail. Anal. 2022, 139, 106454. [Google Scholar] [CrossRef]
- Zhang, K.; Chen, P.; Yang, M.; Song, L.; Xu, Y. The Harmogram: A periodic impulses detection method and its application in bearing fault diagnosis. Mech. Syst. Signal Process. 2022, 165, 108374. [Google Scholar] [CrossRef]
- Boudou, A.; Viguier-Pla, S. Principal components analysis and cyclostationarity. J. Multivar. Anal. 2022, 189, 104875. [Google Scholar] [CrossRef]
- Yang, D.; Karimi, H.R.; Gelman, L. A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks. Sensors 2022, 22, 671. [Google Scholar] [CrossRef]
- Cui, B.; Weng, Y.; Zhang, N. A feature extraction and machine learning framework for bearing fault diagnosis. Renew. Energy 2022, 191, 987–997. [Google Scholar] [CrossRef]
- Raja, H.A.; Kudelina, K.; Asad, B.; Vaimann, T.; Kallaste, A.; Rassõlkin, A.; Khang, H.V. Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines. Energies 2022, 15, 9507. [Google Scholar] [CrossRef]
- Zheng, L.; He, Y.; Chen, X.; Pu, X. Optimization of dilated convolution networks with application in remaining useful life prediction of induction motors. Measurement 2022, 200, 111588. [Google Scholar] [CrossRef]
- Zhang, G.; Wang, Y.; Li, X.; Tang, B.; Qin, Y. Enhanced symplectic geometry mode decomposition and its application to rotating machinery fault diagnosis under variable speed conditions. Mech. Syst. Signal Process. 2022, 170, 108841. [Google Scholar] [CrossRef]
- He, F.; Ye, Q. A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm. Sensors 2022, 22, 1410. [Google Scholar] [CrossRef] [PubMed]
- Arslan, Ö. Automated detection of heart valve disorders with time-frequency and deep features on PCG signals. Biomed. Signal Process. Control 2022, 78, 103929. [Google Scholar] [CrossRef]
- Krikid, F.; Karfoul, A.; Chaibi, S.; Kachenoura, A.; Nica, A.; Kachouri, A.; Jeannès, R.L.B. Classification of High Frequency Oscillations in intracranial EEG signals based on coupled time-frequency and image-related features. Biomed. Signal Process. Control 2022, 73, 103418. [Google Scholar] [CrossRef]
- Barua, M.; Schmidt, G.T.; Yan, L. Improved mechanical fault diagnosis using DCGAN for time–frequency analysis. Mech. Syst. Signal Process. 2023, 189, 109932. [Google Scholar] [CrossRef]
- Jiang, Y.; Niu, G. An iterative frequency-domain envelope-tracking filter for dispersive signal decomposition in structural health monitoring. Mech. Syst. Signal Process. 2022, 179, 109329. [Google Scholar] [CrossRef]
- Fan, H.; Miao, D.; Cheng, M.; Yang, Z.; Zhu, C. Rolling Bearing Fault Diagnosis Using an Enhanced Empirical Mode Decomposition Combined with Weighted Permutation Entropy. Shock Vib. 2021, 2021, 1–14. [Google Scholar]
- Singh, A.; Roe, B.P. A Novel Application of Deep Residual Neural Network for Bearing Fault Diagnosis. IEEE Sens. J. 2020, 20, 1503–1512. [Google Scholar]
- Yu, L.; Yu, L.; Wang, J.; Wang, R.; Chen, Z. Cyclostationary modeling for the aerodynamically generated sound of helicopter rotors. Mech. Syst. Signal Process. 2022, 168, 108680. [Google Scholar] [CrossRef]
- de BA Barros, R.E.; Ebecken, N.F. Development of a ship classification method based on Convolutional neural network and Cyclostationarity Analysis. Mech. Syst. Signal Process. 2022, 170, 108778. [Google Scholar] [CrossRef]
- Cheng, Y.; Wang, S.; Chen, B.; Mei, G.; Zhang, W.; Peng, H.; Tian, G. An improved envelope spectrum via candidate fault frequency optimization-gram for bearing fault diagnosis. J. Sound Vib. 2022, 523, 116746. [Google Scholar] [CrossRef]
- Gupta, A.; Kumar, D.; Verma, H.; Tanveer, M.; Javier, A.P.; Lin, C.T.; Prasad, M. Recognition of multi-cognitive tasks from EEG signals using EMD methods. Neural Comput. Appl. 2023, 35, 22989–23006. [Google Scholar] [CrossRef]
- Gao, Z.; Liu, Y.; Wang, Q.; Wang, J.; Luo, Y. Ensemble empirical mode decomposition energy moment entropy and enhanced long short-term memory for early fault prediction of bearing. Measurement 2022, 188, 110417. [Google Scholar] [CrossRef]
- Xie, S.; Li, Y.; Tan, H.; Liu, R.; Zhang, F. Multi-scale and multi-layer perceptron hybrid method for bearings fault diagnosis. Int. J. Mech. Sci. 2022, 235, 107708. [Google Scholar] [CrossRef]
- Pang, B.; Nazari, M.; Tang, G. Recursive variational mode extraction and its application in rolling bearing fault diagnosis. Mech. Syst. Signal Process. 2022, 165, 108321. [Google Scholar] [CrossRef]
- Sodagudi, S.; Manda, S.; Smitha, B.; Chaitanya, N.; Ahmed, M.A.; Deb, N. EEG signal processing by feature extraction and classification based on biomedical deep learning architecture with wireless communication. Optik 2022, 270, 170037. [Google Scholar] [CrossRef]
- Sun, Y.; Li, S. Bearing fault diagnosis based on optimal convolution neural network. Measurement 2022, 190, 110702. [Google Scholar] [CrossRef]
- Anandhi, B.; Jerritta, S.; Anusuya, I.G.; Das, H. Time Domain Analysis of Heart Rate Variability Signals in Valence Recognition for Children with Autism Spectrum Disorder (ASD). IRBM 2022, 43, 380–390. [Google Scholar] [CrossRef]
- Kumar, J.P.; Chauhan, P.S.; Pandit, P.P. Time domain vibration analysis techniques for condition monitoring of rolling element bearing: A review. Mater. Today Proc. 2022, 62, 6336–6340. [Google Scholar] [CrossRef]
- Wang, Z.; Zhou, J.; Du, W.; Lei, Y.; Wang, J. Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution. Mech. Syst. Signal Process. 2022, 162, 108018. [Google Scholar] [CrossRef]
- Jin, Y.; Hou, L.; Chen, Y. A Time Series Transformer based method for the rotating machinery fault diagnosis. Neurocomputing 2022, 494, 379–395. [Google Scholar] [CrossRef]
- Zhang, K.; Lu, Y.; Shi, H.; Bai, X.; Yuan, Z. The fault position identification method for the outer ring of deep groove ball bearing in a wide range. Measurement 2022, 203, 111939. [Google Scholar] [CrossRef]
- Zhang, K.; Li, H.; Cao, S.; Yang, C.; Sun, F.; Wang, Z. Motor current signal analysis using hypergraph neural networks for fault diagnosis of electromechanical system. Measurement 2022, 201, 111697. [Google Scholar] [CrossRef]
- Kim, J.; Kim, J.; Souza, C.D.D. Discrete time domain analysis of radiation detector noise. Nucl. Instrum. Methods Phys. Res. Sect. Accel. Spectrometers Detect. Assoc. Equip. 2022, 1021, 165925. [Google Scholar] [CrossRef]
- Peng, N.; Liu, X.; Liang, R.; Tang, Z.; Ren, X.; Hu, Y.; Li, G. Edge Computing Based Fault Sensing of the Distribution Cables Based on Time-Domain Analysis of Grounding Line Current Signals. IEEE Trans. Power Deliv. 2022, 37, 4404–4417. [Google Scholar] [CrossRef]
- Hu, T.; Guo, Y.; Gu, L.; Zhou, Y.; Zhang, Z.; Zhou, Z. Remaining useful life prediction of bearings under different working conditions using a deep feature disentanglement based transfer learning method. Reliab. Eng. Syst. Saf. 2022, 219, 108265. [Google Scholar] [CrossRef]
- Althubaiti, A.; Elasha, F.; Teixeira, J.A. Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis—A review. J. Vibroengineering 2021, 24, 46–74. [Google Scholar] [CrossRef]
- Tiboni, M.; Remino, C.; Bussola, R.; Amici, C. A Review on Vibration-Based Condition Monitoring of Rotating Machinery. Appl. Sci. 2022, 12, 972. [Google Scholar] [CrossRef]
- Barai, V.; Ramteke, S.M.; Dhanalkotwar, V.; Nagmote, Y.; Shende, S.; Deshmukh, D. Bearing fault diagnosis using signal processing and machine learning techniques: A review. IOP Conf. Ser. Mater. Sci. Eng. 2022, 1259, 012034. [Google Scholar] [CrossRef]
- Al-hababi, T.; Alkayem, N.F.; Asteris, P.G.; Wang, J.; Hu, S.; Cao, M. Time-frequency domain methods for the identification of breathing cracks in beam-like structures. Tribol. Int. 2023, 180, 108202. [Google Scholar] [CrossRef]
- Miras, J.R.D.; Ibáñez-Molina, A.J.; Soriano, M.F.; Iglesias-Parro, S. Schizophrenia classification using machine learning on resting state EEG signal. Biomed. Signal Process. Control 2023, 79, 104233. [Google Scholar] [CrossRef]
- Wang, C.; Xue, Q.; He, Y.; Wang, J.; Li, Y.; Qu, J. Lithological identification based on high-frequency vibration signal analysis. Measurement 2023, 221, 113534. [Google Scholar] [CrossRef]
- Gour, N.; Hassan, T.; Owais, M.; Ganapathi, I.I.; Khanna, P.; Seghier, M.L.; Werghi, N. Transformers for autonomous recognition of psychiatric dysfunction via raw and imbalanced EEG signals. Brain Inform. 2023, 10, 25. [Google Scholar] [CrossRef] [PubMed]
- Randall, R.B.; Antoni, J. Why EMD and similar decompositions are of little benefit for bearing diagnostics. Mech. Syst. Signal Process. 2023, 192, 110207. [Google Scholar] [CrossRef]
- Chen, Y.; Peng, G.; Zhu, Z.; Li, S. A novel deep learning method based on attention mechanism for bearing remaining useful life prediction. Appl. Soft Comput. 2020, 86, 105919. [Google Scholar] [CrossRef]
- Hou, W.; Zhang, C.; Jiang, Y.; Cai, K.; Wang, Y.; Li, N. A new bearing fault diagnosis method via simulation data driving transfer learning without target fault data. Measurement 2023, 215, 112879. [Google Scholar] [CrossRef]
- Zhang, Q.; Wang, J.; Liu, J.; Li, B.; Wang, C.; Geng, J.; Liu, N. Instantaneous multi-frequency tracker for nonstationary vibration signal in mechanical system. Mech. Syst. Signal Process. 2023, 203, 110695. [Google Scholar] [CrossRef]
- Pla, B.; Morena, J.D.L.; Bares, P.; Aramburu, A. An unsupervised machine learning technique to identify knock from a knock signal time-frequency analysis. Measurement 2023, 211, 112669. [Google Scholar] [CrossRef]
- Dong, H.; Yu, G.; Lin, T.; Li, Y. An energy-concentrated wavelet transform for time-frequency analysis of transient signal. Signal Process. 2023, 206, 108934. [Google Scholar] [CrossRef]
- Golande, A.L.; Pavankumar, T. Optical electrocardiogram based heart disease prediction using hybrid deep learning. J. Big Data 2023, 10, 139. [Google Scholar] [CrossRef]
- Marsick, A.; André, H.; Khelf, I.; Leclère, Q.; Antoni, J. Restoring cyclostationarity of rolling element bearing signals from the instantaneous phase of their envelope. Mech. Syst. Signal Process. 2023, 193, 110264. [Google Scholar] [CrossRef]
- Li, W.; Auger, F.; Zhang, Z.; Zhu, X. Newton time-extracting wavelet transform: An effective tool for characterizing frequency-varying signals with weakly-separated components and theoretical analysis. Signal Process. 2023, 209, 109017. [Google Scholar] [CrossRef]
- Wijayanto, I.; Humairani, A.; Hadiyoso, S.; Rizal, A.; Prasanna, D.L.; Tripathi, S.L. Epileptic seizure detection on a compressed EEG signal using energy measurement. Biomed. Signal Process. Control 2023, 85, 104872. [Google Scholar] [CrossRef]
- Gao, Y.; Wu, H.; Liao, H.; Chen, X.; Yang, S.; Song, H. A fault diagnosis method for rolling bearings based on graph neural network with one-shot learning. EURASIP J. Adv. Signal Process. 2023, 2023, 101. [Google Scholar] [CrossRef]
- Gao, Y.; Zhang, C.; Fang, F.; Cammon, J.; Zhang, Y. Multi-domain feature analysis method of MI-EEG signal based on Sparse Regularity Tensor-Train decomposition. Comput. Biol. Med. 2023, 158, 106887. [Google Scholar] [CrossRef]
- Tang, J.; Sun, X.; Yan, L.; Qu, Y.; Wang, T.; Yue, Y. Sound source localization method based time-domain signal feature using deep learning. Appl. Acoust. 2023, 213, 109626. [Google Scholar] [CrossRef]
- Zhou, P.; Chen, S.; He, Q.; Wang, D.; Peng, Z. Rotating machinery fault-induced vibration signal modulation effects: A review with mechanisms, extraction methods and applications for diagnosis. Mech. Syst. Signal Process. 2023, 200, 110489. [Google Scholar] [CrossRef]
- Ruan, D.; Han, J.; Yan, J.; Gühmann, C. Light convolutional neural network by neural architecture search and model pruning for bearing fault diagnosis and remaining useful life prediction. Sci. Rep. 2023, 13, 5484. [Google Scholar] [CrossRef]
- Nardo, F.D.; Romanato, M.; Spolaor, F.; Volpe, D.; Fioretti, S.; Sawacha, Z. Simplified Muscle-Recruitment Strategy During Walking in Parkinson’s Disease People: A Time-Frequency Analysis of EMG Signal. IRBM 2023, 44, 100798. [Google Scholar] [CrossRef]
- Guo, W.; Li, X.; Wan, X. A novel approach to bearing prognostics based on impulse-driven measures, improved morphological filter and practical health indicator construction. Reliab. Eng. Syst. Saf. 2023, 238, 109451. [Google Scholar] [CrossRef]
- Jawad, S.; Jaber, A. Bearings Health Monitoring Based on Frequency-Domain Vibration Signals Analysis. Eng. Technol. J. 2022, 41, 86–95. [Google Scholar] [CrossRef]
- Peng, C.; Gao, H.; Liu, X.; Liu, B. A visual vibration characterization method for intelligent fault diagnosis of rotating machinery. Mech. Syst. Signal Process. 2023, 192, 110229. [Google Scholar] [CrossRef]
- Zhang, R.; Su, M.; Wang, G.; He, Q. Fault diagnosis and prognosis of rolling bearings with enhanced deep transfer learning: A review. Measurement 2021, 172, 108849. [Google Scholar]
- Rivas, A.; Delipei, G.K.; Davis, I.; Bhongale, S.; Yang, J.; Hou, J. A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation. Reliab. Eng. Syst. Saf. 2024, 247, 110121. [Google Scholar] [CrossRef]
- Shandookh, A.A.; Ogaili, A.A.F.; Al-Haddad, L.A. Failure analysis in predictive maintenance: Belt drive diagnostics with expert systems and Taguchi method for unconventional vibration features. Heliyon 2024, 10, e34202. [Google Scholar] [CrossRef]
- Chu, T.; Nguyen, T.; Yoo, H.; Wang, J. A review of vibration analysis and its applications. Heliyon 2024, 10, e26282. [Google Scholar] [CrossRef]
- Li, X.; Zhang, W.; Ding, Q.; Sun, J.-Q. Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation. J. Intell. Manuf. 2020, 31, 433–452. [Google Scholar] [CrossRef]
- Che, C.; Wang, H.; Fu, Q.; Ni, X. Intelligent fault prediction of rolling bearing based on gate recurrent unit and hybrid autoencoder. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 2021, 235, 1106–1114. [Google Scholar] [CrossRef]
- Jiang, W.; Wang, C.; Zou, J.; Zhang, S. Application of Deep Learning in Fault Diagnosis of Rotating Machinery. Processes 2021, 9, 919. [Google Scholar] [CrossRef]
- Syamsundararao, T.; Selvarani, A.; Rathi, R.; Vini, A.; Grace, N.; Selvaraj, D.; Almutairi, K.M. An Efficient Signal Processing Algorithm for Detecting Abnormalities in EEG Signal Using CNN. Contrast Media Mol. Imaging 2022, 2022, 1–13. [Google Scholar] [CrossRef]
- Zhao, H.; Chen, Z.; Shu, X.; Shen, J.; Liu, Y.; Zhang, Y. Multi-step ahead voltage prediction and voltage fault diagnosis based on gated recurrent unit neural network and incremental training. Energy 2023, 266, 126496. [Google Scholar] [CrossRef]
- Ding, Y.; Jia, M.; Cao, Y.; Ding, P.; Zhao, X.; Lee, C.G. Domain generalization via adversarial out-domain augmentation for remaining useful life prediction of bearings under unseen conditions. Knowl.-Based Syst. 2023, 261, 110199. [Google Scholar] [CrossRef]
- Velásquez, R.M.A. Bearings faults and limits in wind turbine generators. Results Eng. 2024, 21, 101891. [Google Scholar] [CrossRef]
- Fernandez-Blanco, E.; Rivero, D.; Pazos, A. EEG signal processing with separable convolutional neural network for automatic scoring of sleeping stage. Neurocomputing 2020, 410, 220–228. [Google Scholar] [CrossRef]
- Li, Y.; Du, X.; Wan, F.; Wang, X.; Yu, H. Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging. Chin. J. Aeronaut. 2020, 33, 427–438. [Google Scholar] [CrossRef]
- Chen, J.; Lin, C.; Peng, D.; Ge, H. Fault Diagnosis of Rotating Machinery: A Review and Bibliometric Analysis. IEEE Access 2020, 8, 224985–225003. [Google Scholar] [CrossRef]
- Zhang, S.; Zhang, S.; Wang, B.; Habetler, T.G. Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review. IEEE Access 2020, 8, 29857–29881. [Google Scholar] [CrossRef]
- Wang, X.; Shen, C.; Xia, M.; Wang, D.; Zhu, J.; Zhu, Z. Multi-scale deep intra-class transfer learning for bearing fault diagnosis. Reliab. Eng. Syst. Saf. 2020, 202, 107050. [Google Scholar] [CrossRef]
- Huang, M.; Liu, Z.; Tao, Y. Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion. Simul. Model. Pract. Theory 2020, 102, 101981. [Google Scholar] [CrossRef]
- Brown, J.T.; Zgallai, W. Deep EEG: Deep learning in biomedical signal processing with EEG applications. In Biomedical Signal Processing and Artificial Intelligence in Healthcare; Elsevier: Amsterdam, The Netherlands, 2020; pp. 113–151. [Google Scholar] [CrossRef]
- Zhao, K.; Jiang, H.; Li, X.; Wang, R. Ensemble adaptive convolutional neural networks with parameter transfer for rotating machinery fault diagnosis. Int. J. Mach. Learn. Cybern. 2021, 12, 1483–1499. [Google Scholar] [CrossRef]
- Adamsab, K. Machine learning algorithms for rotating machinery bearing fault diagnostics. Mater. Today Proc. 2021, 44, 4931–4933. [Google Scholar] [CrossRef]
- Jin, X.; Chen, Y.; Wang, L.; Han, H.; Chen, P. Failure prediction, monitoring and diagnosis methods for slewing bearings of large-scale wind turbine: A review. Measurement 2021, 172, 108855. [Google Scholar] [CrossRef]
- Bustos, A.; Rubio, H.; Soriano-Heras, E.; Castejon, C. Methodology for the integration of a high-speed train in Maintenance 4.0. J. Comput. Des. Eng. 2021, 8, 1605–1621. [Google Scholar] [CrossRef]
- Yang, Y.; Haque, M.M.M.; Bai, D.; Tang, W. Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review. Energies 2021, 14, 7017. [Google Scholar] [CrossRef]
- Ghazali, M.H.; Rahiman, W. Vibration Analysis for Machine Monitoring and Diagnosis: A Systematic Review. Shock Vib. 2021, 2021, 9469318. [Google Scholar] [CrossRef]
- Sun, Y.; Feng, T.; Jin, Z. Review on Vibration Signal Analysis of Rotating Machinery Based on Deep Learning. J. Phys. Conf. Ser. 2021, 1820, 012034. [Google Scholar] [CrossRef]
- Vairachilai, S.; Bostani, A.; Mehbodniya, A.; Webber, J.L.; Hemakesavulu, O.; Vijayakumar, P. Body Sensor 5 G Networks Utilising Deep Learning Architectures for Emotion Detection Based On EEG Signal Processing. Optik 2022, 170469. [Google Scholar] [CrossRef]
- Hosseini, M.-P.; Hosseini, A.; Ahi, K. A Review on Machine Learning for EEG Signal Processing in Bioengineering. IEEE Rev. Biomed. Eng. 2021, 14, 204–218. [Google Scholar] [CrossRef]
- Kumar, R.R.; Andriollo, M.; Cirrincione, G.; Cirrincione, M.; Tortella, A. A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors. Energies 2022, 15, 8938. [Google Scholar] [CrossRef]
- Tama, B.A.; Vania, M.; Lee, S.; Lim, S. Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals. Artif. Intell. Rev. 2022, 56, 4667–4709. [Google Scholar] [CrossRef]
- Junior, R.F.R.; Areias, I.A.d.S.; Campos, M.M.; Teixeira, C.E.; da Silva, L.E.B.; Gomes, G.F. Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals. Measurement 2022, 190, 110759. [Google Scholar] [CrossRef]
- Zhu, Z.; Lei, Y.; Qi, G.; Chai, Y.; Mazur, N.; An, Y.; Huang, X. A review of the application of deep learning in intelligent fault diagnosis of rotating machinery. Measurement 2023, 206, 112346. [Google Scholar] [CrossRef]
- Xiao, Y.; Shao, H.; Feng, M.; Han, T.; Wan, J.; Liu, B. Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in transformer. J. Manuf. Syst. 2023, 70, 186–201. [Google Scholar] [CrossRef]
- Gawde, S.; Patil, S.; Kumar, S.; Kamat, P.; Kotecha, K.; Abraham, A. Multi-fault diagnosis of Industrial Rotating Machines using Data-driven approach: A review of two decades of research. Eng. Appl. Artif. Intell. 2023, 123, 106139. [Google Scholar] [CrossRef]
- Ruan, D.; Wang, J.; Yan, J.; Gühmann, C. CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis. Adv. Eng. Inform. 2023, 55, 101877. [Google Scholar] [CrossRef]
- Pan, Z.; Zhang, Z.; Meng, Z.; Wang, Y. A novel fault classification feature extraction method for rolling bearing based on multi-sensor fusion technology and EB-1D-TP encoding algorithm. ISA Trans. 2023, 142, 427–444. [Google Scholar] [CrossRef]
- Huo, C.; Jiang, Q.; Shen, Y.; Lin, X.; Zhu, Q.; Zhang, Q. A class-level matching unsupervised transfer learning network for rolling bearing fault diagnosis under various working conditions. Appl. Soft Comput. 2023, 146, 110739. [Google Scholar] [CrossRef]
- Glowacz, A. Thermographic fault diagnosis of electrical faults of commutator and induction motors. Eng. Appl. Artif. Intell. 2023, 121, 105962. [Google Scholar] [CrossRef]
- AlShorman, O.; Irfan, M.; Abdelrahman, R.B.; Masadeh, M.; Alshorman, A.; Sheikh, M.A.; Saad, N.; Rahman, S. Advancements in condition monitoring and fault diagnosis of rotating machinery: A comprehensive review of image-based intelligent techniques for induction motors. Eng. Appl. Artif. Intell. 2024, 130, 107724. [Google Scholar] [CrossRef]
- Williams, L.; Phillips, C.; Sheng, S.; Dobos, A.; Wei, X. Scalable Wind Turbine Generator Bearing Fault Prediction Using Machine Learning: A Case Study. In Proceedings of the 2020 IEEE International Conference on Prognostics and Health Management (ICPHM), Detroit, MI, USA, 8–10 June 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 1–9. [Google Scholar] [CrossRef]
- Achouch, M.; Dimitrova, M.; Ziane, K.; Sattarpanah Karganroudi, S.; Dhouib, R.; Ibrahim, H.; Adda, M. On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Appl. Sci. 2022, 12, 8081. [Google Scholar] [CrossRef]
- Liu, J.; Pan, C.; Lei, F.; Hu, D.; Zuo, H. Fault prediction of bearings based on LSTM and statistical process analysis. Reliab. Eng. Syst. Saf. 2021, 214, 107646. [Google Scholar] [CrossRef]
- Righetto, S.B.; Marcos, A.I.M.; Carvalho, E.G.; Hattori, L.T.; de Francisci, S. Predictive Maintenance 4.0 Applied in Electrical Power Systems. In Proceedings of the 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 16–18 February 2021. [Google Scholar]
- Swider, A.; Pedersen, E. Comparison of delayless digital filtering algorithms and their application to multi-sensor signal processing. Trans. Inst. Meas. Control 2018, 41, 2338–2351. [Google Scholar] [CrossRef]
- Nordio, A.; Chiasserini, C.; Viterbo, E. Signal reconstruction errors in jittered sampling. IEEE Trans. Signal Process. 2009, 57, 4711–4718. [Google Scholar] [CrossRef]
- Walden, R. Analog-to-digital conversion in the early twenty-first century. Wiley Encycl. Comput. Sci. Eng. 2008, 1–14. [Google Scholar] [CrossRef]
- Dempster, J. Signal Analysis and Measurement. In The Laboratory Computer; Academic Press: Cambridge, MA, USA, 2001; pp. 136–171. [Google Scholar] [CrossRef]
- Goyal, D.; Pabla, B.S. Condition based maintenance of machine tools—A review. CIRP J. Manuf. Sci. Technol. 2015, 10, 24–35. [Google Scholar] [CrossRef]
- Zhu, K.; Wong, Y.; San, H.; Geok, S. 4—Signal processing for tool condition monitoring: From wavelet analysis to sparse decomposition. In Woodhead Publishing Reviews: Mechanical Engineering Series, Mechatronics and Manufacturing Engineering; Davim, J.P., Ed.; Woodhead Publishing: Sawston, UK, 2012; pp. 115–157. ISBN 9780857091505. [Google Scholar] [CrossRef]
- Chaleshtori, A.E.; Aghaie, A. A novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis. Reliab. Eng. Syst. Saf. 2024, 242, 109720. [Google Scholar] [CrossRef]
- Sivakumar, S.; Nedumaran, D. Discrete time-frequency signal analysis and processing techniques for non-stationary signals. J. Appl. Math. Phys. 2018, 6, 1916–1927. [Google Scholar] [CrossRef]
- Li, Y.; Zheng, X. Wigner-ville distribution and its application in seismic attenuation estimation. Appl. Geophys. 2007, 4, 245–254. [Google Scholar] [CrossRef]
- Guo, B.; Song, S.; Ghalambor, A.; Lin, T.R. Chapter 17—An Introduction to Condition-Based Maintenance, 2nd ed.; Guo, B., Song, S., Ghalambor, A., Lin, T.R., Pipelines, O., Eds.; Gulf Professional Publishing: Boston, MA, USA, 2014; pp. 257–297. ISBN 9780123979490. [Google Scholar] [CrossRef]
- Córdova, F.; Cifuentes, F.; Díaz, H. The Hilbert-Huang Transform as a method and tool to support the analysis of non-linear and non-stationary electroencephalographic signals. In Proceedings of the 2022 IEEE International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA), Curicó, Chile, 24–28 October 2022. [Google Scholar] [CrossRef]
- Gao, X.; Tao, L. Gabor time–frequency representation for transient signals using multiwindow discrete Gabor transform. Int. J. Wavelets, Multiresolution Inf. Process. 2017, 15, 1750036. [Google Scholar] [CrossRef]
- Mohammad, N.H.; Amini, M. Gabor-Fourier Analysis. In Fourier Transform-Signal Processing and Physical Sciences; IntechOpen: London, UK, 2015. [Google Scholar] [CrossRef]
- Volkov, I.A.; Vladimir; Priputin, S. Adaptive Signal Decomposition Methods. In Proceedings of the 2024 Systems of Signals Generating and Processing in the Field of on Board Communications, Moscow, Russian Federation, 12–14 March 2024. [Google Scholar] [CrossRef]
- Miao, Q.; Shu, Q.; Wu, B.; Sun, X.; Song, K. A Modified Complex Variational Mode Decomposition Method for Analyzing Nonstationary Signals with the Low-Frequency Trend. Sensors 2022, 22, 1801. [Google Scholar] [CrossRef]
- Shen, X.; Li, R. BroadBand-Adaptive VMD with Flattest Response. Mathematics 2023, 11, 1858. [Google Scholar] [CrossRef]
- Chen, L.; Wan, Z.; Liu, N.; Lu, X. Reduced-order variational mode decomposition to reveal transient and non-stationary dynamics in fluid flows. J. Fluid Mech. 2023, 966, A7. [Google Scholar] [CrossRef]
- Padhmashree, V.; Bhattacharyya, A. Human emotion recognition based on time–frequency analysis of multivariate EEG signal. Knowl.-Based Syst. 2022, 238, 107867. [Google Scholar] [CrossRef]
- Kakarla, M.; Raju, K.P. Review of Machine learning: Views, Architectures or Techniques, Challenges and Future guidance and Real-world applications. Int. Res. J. Adv. Eng. Manag. (IRJAEM) 2024, 2, 545–551. [Google Scholar] [CrossRef]
- Chen, K. Research on Popular Machine Learning Algorithms. In Proceedings of the 2023 IEEE 6th International Conference on Information Systems and Computer Aided Education (ICISCAE), Dalian, China, 23–25 September 2023. [Google Scholar] [CrossRef]
- Moreno, G. Support Vector Machine Algorithm in Machine Learning. In Proceedings of the 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China, 24–26 June 2022. [Google Scholar] [CrossRef]
- Munir, A.; Kong, J.; Qureshi, M.A. Overview of Convolutional Neural Networks; WILEY: Hoboken, NJ, USA, 2023. [Google Scholar] [CrossRef]
- Hua, Y.; Guo, Y.; Zhao, H. Deep Belief Networks and deep learning. In Proceedings of the 2015 International Conference on Intelligent Computing and Internet of Things, Harbin, China, 17–18 January 2015. [Google Scholar] [CrossRef]
- Dinov, I.D. Deep Learning, Neural Networks. In Data Science and Predictive Analytics; Springer International Publishing: Berlin/Heidelberg, Germany, 2023; pp. 773–901. ISBN 9783031174834. [Google Scholar] [CrossRef]
- Visali, P.R.; Shwetha, L.G.; Sri, N.; Raja, M. Preliminary big data analytics of hepatitis disease by random forest and SVM using r-tool. In Proceedings of the 2017 Third International Conference on Biosignals, Images and Instrumentation (ICBSII), Chennai, India, 16–18 March 2017. [Google Scholar] [CrossRef]
- Xia, Z. Overfitting of CNN model in cifar-10: Problem and solutions. Appl. Comput. Eng. 2024, 37, 212–221. [Google Scholar] [CrossRef]
- Xie, S.; Li, L. Improvement and Application of Deep Belief Network Based on Sparrow Search Algorithm. In Proceedings of the 2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China, 27–28 August 2021. [Google Scholar] [CrossRef]
- Nghiem, T.-L.; Le, V.D.; Le, T.-L.; Maréchal, P.; Delahaye, D.; Vidosavljevic, A. Applying Bayesian inference in a hybrid CNN-LSTM model for time-series prediction. In Proceedings of the 2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), Phu Quoc, Vietnam, 13–14 October 2022. [Google Scholar] [CrossRef]
- Li, H.T.; Krishnan, G.; Lin, R.D.; Bezemer, C.; Jiang, Z.M. Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting. IEEE Access 2024, 12, 70676–70689. [Google Scholar] [CrossRef]
- Wan, L.; Li, H.; Chen, Y.; Li, C. Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory. Energies 2020, 13, 1094. [Google Scholar] [CrossRef]
- Hu, A.; Zhu, Y.; Liu, S.; Xing, L.; Xiang, L. A novel vision transformer network for rolling bearing remaining useful life prediction. Meas. Sci. Technol. 2023, 35, 025106. [Google Scholar] [CrossRef]
- Yi’an, Z.; Ziming, H.; Yongfang, M.; Yi, C.; Le, M. Bearing Remaining Useful Life Prediction based on TCN-Transformer Model. In Proceedings of the 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Yibin, China, 22–24 September 2023. [Google Scholar] [CrossRef]
- Case Western Reserve University. Case Western Reserve University Bearing Data Center. Available online: https://engineering.case.edu/bearingdatacenter (accessed on 27 November 2023).
- Lee, J.; Qiu, H.; Yu, G.; Lin, J.; Rexnord Technical Services. IMS, University of Cincinnati. “Bearing Data Set”, NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA. 2007. Available online: https://www.nasa.gov/content/prognostics-center-of-excellence-data-set-repository (accessed on 19 December 2023).
- Yu, P.; Ping, M.; Cao, J. An Interpretable Deep Learning Approach for Bearing Remaining Useful Life. In Proceedings of the 2023 China Automation Congress (CAC), Chongqing, China, 17–19 November 2023; pp. 6199–6204. [Google Scholar] [CrossRef]
- Machinery Failure Prevention Technology Society. MFPT Bearing Dataset. Available online: https://www.mfpt.org/fault-data/ (accessed on 19 December 2023).
- Southeast University. SEU Gearbox Fault Dataset. Available online: https://github.com/cathysiyu/Mechanical-datasets (accessed on 12 January 2024).
- Paderborn University. PU Bearing Dataset. Available online: https://mb.uni-paderborn.de/kat/forschung/kat-datacenter/bearing-datacenter/data-sets-and-download/ (accessed on 20 February 2024).
- Nectoux, P.; Gouriveau, R.; Medjaher, K.; Ramasso, E.; Chebel-Morello, B.; Zerhouni, N.; Varnier, C. PRONOSTIA: An experimental platform for bearing accelerated degradation tests. In Proceedings of the IEEE International Conference on Prognostics and Health Management (PHM), Denver, CO, USA, 18–21 June 2012; pp. 1–8. Available online: https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/ (accessed on 19 December 2023).
- Xi’an Jiaotong University and Changxing Sumyoung Technology Co., Ltd. XJTU-SY Bearing Dataset. Available online: https://biaowang.tech/xjtu-sy-bearing-datasets/ (accessed on 11 January 2024).
- ISO 13373-1:2002; Condition Monitoring and Diagnostics of Machines—Vibration Condition Monitoring—Part 1: General Procedures. ISO: Geneva, Switzerland, 2002.
Main Objective | Preprocessing | Processing | Post-Processing | Diagnosis | Prognosis | Experimental Validation |
---|---|---|---|---|---|---|
Question asked | Were the data preprocessed? | How were the data processed? | How were the results of the processing optimized? | Which AI tools were used? | Which AI tools were used? | Were the results validated experimentally? |
PICO Element | Corresponding Element | Synonym 1 | Synonym 2 | Synonym 3 | Synonym 4 |
---|---|---|---|---|---|
P (Problem) | Rotating machinery | Rotating Equipment | Turbines | Motors | Generators |
I (Intervention/Technology) | Vibration signal analysis | Vibration analysis | Vibration signal processing | Condition monitoring | |
C (Comparison/Techniques) | Intelligent solutions | Artificial Intelligence | Deep learning | Intelligent algorithms | Machine learning |
O (Outcome) | Diagnosis and prognosis of machinery conditions | Fault diagnosis | Failure prediction | Prognosis | Predictive maintenance |
PICO Element | Corresponding Element | Synonym 1 | Synonym 2 |
---|---|---|---|
P (Population/Problem) | Rotating machinery | Rotating equipment | Bearing |
I (Intervention/Technology) | Vibration signal processing | ||
C (Comparison/Techniques) | Intelligent solutions | Deep learning | Machine learning |
O (Outcome/Goal) | Prognosis of machinery conditions | RUL prediction | Failure prediction |
Search Query 1 | Search Query 2 |
---|---|
(“rotating machinery” OR “rotating equipment” OR “turbines” OR “motors” OR “generators”) AND (“vibration analysis” OR “vibration monitoring” OR “vibration signal processing”) AND (“fault diagnosis” OR “failure prediction” OR “prognosis” OR “predictive maintenance”) AND (“artificial intelligence” OR “machine learning” OR “intelligent algorithms” OR “deep learning”) | (“rotating machinery” OR “rotating equipment” OR “bearing”) AND (“vibration analysis” OR “vibration signal processing”) AND (“RUL Prediction” OR “failure prediction”) AND (“machine learning” OR “deep learning”) |
Article Reference | Type of Study | Preprocessing | Processing Domain | Processing Method | Post-Processing | Diagnosis | Prognosis | Experimental Validation |
---|---|---|---|---|---|---|---|---|
[23] | Prototype | No | Time scale, time, and frequency domains | CEEMDAN decomposition, statistical features, and FFT | chi-square-RFE method | SVM, ELM, DBN, and DNN | No | No |
Feature Selection Approach | Algorithm | Studies |
---|---|---|
Fuzzy-Logic- and Kernel-Based | Fuzzy Logic Embedded RBF-Kernel-Based ELM (FRBFELM) | [60] |
Neural-Network- and CNN-Based | Convolutional Neural Networks (CNNs) | [29,51,116] |
Heuristic Search Methods | Gray Wolf Optimizer | [147] |
Cuckoo Search (CS) | [56] | |
Monotony Evaluation | [86,167] | |
Adaptive Multiscale Convolutions | Stacked Residual Adaptive Multiscale Convolution (Res AM) Blocks | [75] |
Multiscale Convolutional Strategy | [162] | |
Genetic Algorthms | Genetic Algorithms (GAs) | [41,94,134] |
Sequential and Recursive Selection | Sequential Forward Floating Selection (SFFS) | [27] |
Recursive Feature Elimination (RFE) | [23,188,225] | |
Sequential Backward Feature Selection (SBFS) | [44] | |
Optimization-Based Approaches | Expectation Selection Maximization (ESM) | [286] |
Correlation-Based Feature Selection (CFS) | [157] | |
Discriminant Regularizer with Gradient Descent | [145] |
Feature Selection Approach | Method | Studies |
---|---|---|
Statistical Correlation Analysis | Statistical Correlation | [42,53,68,80] |
Pearson’s Correlation | [198,246] | |
Connection Weights and Fisher’s Criterion | Connection Weights | [43,245] |
Fisher’s Criterion | [129] | |
Correlation Coefficients | Correlation Coefficients Calculated for Intrinsic Mode Functions (IMFs) | [126] |
Correlation Metrics | [65,86] | |
Advanced Correlation Techniques | Gray Relation Analysis (GRA) | [185] |
Differential Evolution | [118] | |
Discriminating Capability | [165] | |
Frequency Spectrum Averaging | [47] | |
Acceleration Responses and Wavelet Scalograms | [227] | |
Novel Techniques | Frequency Band Entropy (FBE) and Envelope Power Spectrum Analysis for Selecting the Optimal Intrinsic Mode Functions (IMFs) | [130] |
Feature Selection Approach | Method | Studies |
---|---|---|
PCA Variants | Principal Component Analysis (PCA) | [24,28,40,74,86,214,216] |
K-Principal Component Analysis (K-PCA) | [28,60,136] | |
Weighted Principal Component Analysis (WPCA) | [286] | |
Singular Value Decomposition | Multi-Weight Singular Value Decomposition (MWSVD) | [157] |
Singular Value Decomposition (SVD) | [68,136] | |
Neighborhood Component Analysis | Neighborhood Component Analysis (NCA) | [183] |
Non-Linear Dimensionality Reduction | T-Distributed Stochastic Neighbor Embedding (t-SNE) | [105] |
Category of Classifiers | Algorithm | Studies |
---|---|---|
Neural-Network-Based Classifiers | Graph Neural Network (GNNs) | [229] |
Deep Neural Networks (DNNs) | [23] | |
Recurrent Neural Networks (RNNs) | [260,268] | |
Generative Adversarial Networks (GANs) | [268] | |
Spiking Neural Networks (SNNs) | [57] | |
A Stacked Inverted Residual Convolution Neural Network (SIRCNN) | [108] | |
Class-Level Matching Transfer Learning Network | [273] | |
Statistical- and Clustering-Based Classifiers | Fuzzy-Logic-Based Confidence Decision | [78] |
K-Means Clustering | [153] | |
Discriminant Analysis Classifier (DAC) | [160] | |
Gaussian Mixture Model (GMM) | [286] | |
Local Binary Pattern (LBP) | [133] |
Category of Classifiers | Algorithm | Studies |
---|---|---|
Neural-Network- and Attention-Based | Bayesian Quadratic Discriminant Transfer Neural Network (BQDTNN) | [201] |
Multi-Head-Attention-Based Long Short-Term Memory (MHA-LSTM) | [263] | |
Fuzzy Logic and Statistical Methods | Fuzzy RBF-ELM Classifier | [60] |
Gaussian Discriminant Analysis (GDA) | [28] | |
Ensemble Methods | EBT Classifiers | [190] |
Ensemble Classifier Algorithms | [44,203] |
Classifier | Advantages | Disadvantages | Scalability |
---|---|---|---|
SVM | Effective in high-dimensional cases | Complex kernel selection | Small datasets |
Non-linear separation | Long training time | [a few hundred to a few thousand training samples] | |
Efficient memory usage | Difficult to interpret the model | ||
Generalization (robust even with biased training samples) | Hyperparameter tuning | ||
CNN | Excellent feature extraction | Data requirements (large labeled datasets) | Large datasets |
Temporal and spatial analysis | Difficult to interpret | [thousands to millions of training samples] | |
Efficient learning | |||
LSTM | Handling long-term dependencies | Training challenges | Large datasets |
Well suited to time series analysis | (need to balance the learning of short-term and long-term dependencies in signals) | [few thousand to millions of training samples] | |
Robust to noise | |||
KNN | Simplicity | Sensitive to outliers | Small datasets |
Non-parametric | Need for optimal k | [a few hundred to a few thousand training samples] | |
Transparent decision-making process | Memory-intensive | ||
RF | Robustness | Long training time | Wide range of dataset sizes |
Feature importance | Hyperparameter tuning required | [a few hundred to millions of training samples] | |
Memory-intensive | |||
DBN | Unsupervised feature learning | Challenging to interpret | Large datasets |
Handling complex patterns | Sensitivity to hyperparameters | [a few thousand to millions of training samples] | |
Robustness to noise | (number of layers, number of units, learning rate) | ||
Versatility |
Algorithm | Complexity Ranking | Accuracy Ranking | Computational Cost |
---|---|---|---|
SVM | 3 | 2 | 3 |
CNN | 5 | 5 | 5 |
LSTM | 5 | 4 | 5 |
KNN | 1 | 3 | 2 |
RF | 3 | 1 | 3 |
DBN | 4 | 6 | 5 |
Category of Classifiers | Algorithm | Studies |
---|---|---|
Common Machine Learning Algorithms | K-Nearest Neighbors (KNN) | [184] |
Random Forest Along with Linear Regression and Gradient Boosting Model | [276] | |
Long Short-Term Memory (LSTM) | ||
Artificial Neural Networks (ANNs) | [100,136] | |
Advanced and Hybrid Machine Learning | Wavelet Neural Network (WNN) | [73] |
Stacking Ensemble Model | [84] | |
Quantum Particle Swarm Optimization (QPSO) with Backpropagation (BP) Neural Network | [309] | |
Dempster–Shafer Evidence Theory | [309] | |
Stacked Layer Deep Neural Network (DNN) with Gaussian Window | [90] | |
Stacked Autoencoder and Recurrent Neural Network (RNN) | [167,220] | |
Adaptive Neuro-Fuzzy Inference System (ANFIS) | [39,111] | |
Neuro-Fuzzy Network (NFN) | [111] | |
Gate Recurrent Unit (GRU) | [246] | |
Hybrid Autoencoder | [243] | |
Auto Deep Neural Network (AutoDNN) | [83] |
Database | Studies |
---|---|
CWRU [312] | [34,53,57,75,83,114,144,164,229,233,243,271,286] |
IMS [313] | [41,68,198,235,239,314] |
MFPT bearing dataset [315] | [57,182,268] |
SEU gearbox dataset [316] | [268] |
PU bearing dataset [317] | [182] |
PRONOSTIA dataset [318] | [169,171,198,211,247,252] |
XJTU-SY dataset [319] | [170,171,235,247] |
Paderborn University dataset [317] | [57,75,273] |
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Bagri, I.; Tahiry, K.; Hraiba, A.; Touil, A.; Mousrij, A. Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review. Vibration 2024, 7, 1013-1062. https://doi.org/10.3390/vibration7040054
Bagri I, Tahiry K, Hraiba A, Touil A, Mousrij A. Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review. Vibration. 2024; 7(4):1013-1062. https://doi.org/10.3390/vibration7040054
Chicago/Turabian StyleBagri, Ikram, Karim Tahiry, Aziz Hraiba, Achraf Touil, and Ahmed Mousrij. 2024. "Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review" Vibration 7, no. 4: 1013-1062. https://doi.org/10.3390/vibration7040054