Search Results (291)

The wheel hub planetary gearset, a reduction gearbox of driven axles, is a susceptible driveline unit thanks to the number of gear meshes and its high manufacturing requirements. Due to its complexity, the planet carrier can be a potential source of functional parameter non-compliance (efficiency, noise, and vibration) in the short term; in the long term, there can be a reduction in the service life of the planetary gearset. This paper introduces a real case study that analyzes the calculated and experimental effects of planet carrier machining quality from a functional property and service life point of view, and compares two parts, which represent the original and the developed status of the pin hole drilling process. Full article

As a type of locally resonant phononic crystal, alloy steel phononic crystals have achieved notable advancements in vibration and noise reduction, particularly in the realm of low-frequency noise. Their exceptional band gap characteristics enable the efficient reduction of vibration and noise at low frequencies. However, the conventional transmission loss (TL) simulation of finite structures remains the benchmark for plate structure TL experiments. In this context, the TL in the XY-direction of phononic crystal plate structures has been thoroughly investigated and analyzed. Given the complexity of sound wave incident directions in practical applications, the conventional TL simulation of finite structures often diverges from reality. Taking tungsten steel phononic crystals as an example, this paper introduces a novel finite element method (FEM) simulation approach for analyzing the TL of alloy steel phononic crystal plates. By setting the Z-direction as the excitation source, the tungsten steel phononic crystal plate exhibits distinct responses compared to excitation in the XY-direction. By combining energy band diagrams and modes, the impact of various excitation source directions on the TL simulations is analyzed. It is observed that the tungsten steel phononic crystal plate exhibits a more pronounced energy response under longitudinal excitation. The TL map excited in the Z-direction lacks the flat region present in the XY-direction TL map. Notably, the maximum TL in the Z-direction is 131.5 dB, which is significantly lower than the maximum TL of 298 dB in the XY-direction, with a more regular peak distribution. This indicates that the TL of alloy steel phononic crystals in the XY-direction is closely related to the acoustic wave propagation characteristics within the plate, whereas the TL in the Z-direction aligns more closely with practical sound insulation and noise reduction engineering applications. Therefore, future research on alloy steel phononic crystal plates should not be confined to the TL in the XY-direction. Further investigation and analysis of the TL in the Z-direction are necessary. This will provide a novel theoretical foundation and methodological guidance for future research on alloy steel phononic crystals, enhancing the completeness and systematicness of studies on alloy steel phononic crystal plates. Simultaneously, it will advance the engineering application of alloy steel phononic crystal plates. Full article

To reduce the low-frequency noise inside automobiles, a lightweight plate-type locally resonant acoustic metamaterial (LRAM) is proposed. The design method for the low-frequency bending wave bandgap of the LRAM panel was derived. Prototype LRAM panels were fabricated and tested, and the effectiveness of the bandgap design was verified by measuring the vibration transmission characteristics of the steel panels with the installed LRAM. Based on the bandgap design method, the influence of geometric and material parameters on the bandgap of the LRAM panel was investigated. The LRAM panel was installed on the inner side of the tailgate of a traditional SUV, which effectively reduced the low-frequency noise (around 34 Hz) during acceleration and constant-speed driving, improving the subjective perception of the low-frequency noise from “very unsatisfactory” to “basically satisfactory”. Furthermore, the noise reduction performance of the LRAM panel was compared with that of traditional damping panels. It was found that, with a similar installation area and lighter weight than the traditional damping panels, the LRAM panel still achieved significantly better low-frequency noise reduction, exhibiting the advantages of lightweight, superior low-frequency performance, designable bandgap and shape, and high environmental reliability, which suggests its great potential for low-frequency noise reduction in vehicles. Full article

In today’s industry, due to the importance placed on occupational health and safety, regulations are being implemented to reduce noise in factory conditions. This study aims to reduce fan noise levels while simultaneously enhancing fan efficiency. We focused specifically on the evaporator fans used in the ammonia refrigeration tunnels at Unilever’s Algida ice cream production facility. Both physical and computational models of the fan blades were developed using Fluent and Phoenics computational fluid dynamics (CFD) software. Vibration analyses of the fan blades were performed using the modal analysis module. Through the analysis of pressure distributions on the existing fan blades, it was found that aerodynamic irregularities were the primary contributors to the noise. To address this, the NACA 6412 airfoil model was selected for the redesign, resulting in balanced pressure distribution across the blade surfaces and a reduction in noise levels from 96 dB to 78 dB. Full article

Under “dual-carbon” goals and rapid renewable energy growth, increasing start-stop frequency poses new challenges to safe operations of pumped-storage power plant equipment. Ensuring equipment safety and predictive maintenance under complex conditions urgently requires vibration warnings and trend forecasting for pumped-storage units. In this study, the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. We monitored the vibration-signal data of hydroelectric units over a long period of time, and the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are accurately obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. In this paper, a BP neural network prediction model, a support vector machine (SVM) prediction model, a convolutional neural network (CNN) prediction model, and a long short-term memory network (LSTM) prediction model are used to predict the trend of vibration signals of the pumped-storage unit under different operating conditions. The model prediction effect is analyzed by using the different error evaluation functions, and the prediction results are compared with the predicted results of the four different methods. By comparing the prediction effects of the four different methods, it is concluded that LSTM has higher prediction accuracy and can predict the vibration trends of hydropower units more accurately. Full article

The diagnosis of vibration faults in hydropower units is essential for ensuring the safe and stable operation of these systems. This paper proposes a fault diagnosis method for hydropower units that combines Dynamic Mode Decomposition (DMD) with an optimized Extreme Learning Machine (ELM) utilizing the Hiking Optimization Algorithm (HOA). To address the issue of noise interference in the vibration signals of hydropower units, this study employs DMD technology alongside a thresholding technique for noise reduction, demonstrating its effectiveness through comparative trials. Furthermore, to facilitate a thorough analysis of the operational status of hydropower units, this paper extracts multidimensional features from denoised signals. To improve the efficiency of model training, Principal Component Analysis (PCA) is applied to streamline the data. Given that the weights and biases of the ELM are generated randomly, which may impact the model’s stability and generalization capabilities, the HOA is introduced for optimization. The HOA-ELM model achieved a classification accuracy of 95.83%. A comparative analysis with alternative models substantiates the superior performance of the HOA-ELM model in the fault diagnosis of hydropower units. Full article

Floor impact noise is a significant social concern to secure a quiescent living space for multi-story building residents in South Korea. The floating floor, consisting of a concrete structure on resilient pads, is a specifically designed system to minimize noise transmission. This floating structure employs polymeric pads as the resilient materials. In this study, we investigated the utilization of helically shaped machining scraps as a resilient material for an alternative approach to floor noise reduction. The dynamic elastic modulus and loss factor of the scrap pads were measured using the vibration test method. The scrap pads exhibited a low dynamic elastic modulus and a high loss factor compared to the polymeric pads. Heavyweight impact sound experiments in an actual building were conducted to evaluate the noise reduction performance. The proposed pads showed excellent performance on the reduction in the structure-borne vibration of the concrete slab and resulting sound generation. The analytical model was used to simulate the response of the floating floor structure, enabling a parametric study to examine the effects of the resilient layer viscoelastic properties. Both experimental and analytical evidence confirmed that the proposed scrap pads contribute to the development of sustainable solutions for the minimization of floor impact noise. Full article

Predicting the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and availability of mechanical systems. Constructing health indicators (HIs) is a fundamental step in the methodology for predicting the RUL of rolling bearings. Traditional HI construction often involves determining the degradation stage of the bearing by extracting time–frequency domain features from raw data using a priori knowledge and setting artificial thresholds; this approach does not fully utilize the vibration information in the bearing data. In order to address the above problems, this paper proposes an Asymmetric Residual Shrinkage Convolutional Autoencoder (ARSCAE) model. The asymmetric structure of the ARSCAE model is characterized by the soft thresholding of signal features in the encoder part to achieve noise reduction. The decoder part consists of convolutional and pooling layers for data reconstruction. This model can directly construct HIs from the original vibration signals collected, and comparisons with other models show that it constructs better HIs from the original vibration signals. Finally, experiments on the FEMTO dataset show that the results indicate that the HIS constructed by the ARSCAE model has better lifetime prediction capability compared to other methods. Full article

To accurately predict the remaining useful life (RUL) of rolling bearings under limited data and fluctuating load conditions, we propose a new method for constructing health indicators (HI) and a transfer learning prediction framework, which integrates Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Multi-head attention (MHA). Firstly, we combined Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) to fully extract temporal and spatial features from vibration signals. Then, the Multi-head attention mechanism (MHA) was added for weighted processing to improve the expression ability of the model. Finally, a new method for constructing Health indicators (HIs) was proposed in which the noise reduction and normalized vibration signals were taken as a HI, the L1 regularization method was added to avoid overfitting, and the model-based transfer learning method was used to realize the RUL prediction of bearings under small samples and variable load conditions. Experiments were conducted using the PHM2012 dataset from the FEMTO-ST research institute and XJTU-SY dataset. Three sets of 12 migration experiments were conducted under three different operating conditions on the PHM2012 dataset. The results show that the average RMSE of the proposed method was 0.0443, indicating high prediction accuracy under variable loads and small sample conditions. Three different operating conditions and two sets of four migration experiments were conducted on the XJTU-SY dataset, and the results show that the average RMSE of the proposed method was 0.0693, verifying the good generalization of the model under variable load conditions. In summary, the proposed HI construction method and prediction framework can effectively reduce the differences between features, with high stability and good generalizability. Full article

Torque ripple and radial electromagnetic (EM) vibration can lead to motor vibration and noise, which are crucial to the motor’s NVH (Noise, Vibration, and Harshness) performance. Researchers focus on two main aspects: motor body design and control strategy, employing various methods to optimize the motor and reduce torque ripple and radial EM vibration. Rotor notching and segmented rotor skewing are frequently used techniques. However, determining the optimal notch and skew strategy has been an ongoing challenge for researchers. In this paper, an 8-pole, 36-slot ISG motor is optimized using a combination of Q-axis and magnetic bridge notching (QMC notch) as well as segmented rotor skewing to reduce torque ripple and radial EM vibration. Three skewing strategies—step skew (SS), V-shape skew (VS), and zigzag skew (ZS)—along with four segmentation cases are thoroughly considered. The results show that the QMC notch significantly reduces torque ripple, while skewing designs greatly diminish radial EM vibrations. However, at 14 $f_e$, the EM vibration frequency is close to the motor’s third-order natural frequency, leading to mixed results in vibration reduction using skewing techniques. After a comprehensive analysis of all skewing strategies, four-segment VS and ZS are recommended as the optimal approaches. Full article

Pumped storage units serve as a crucial support for power systems to adapt to large-scale and high-proportion renewable energy sources by providing a stable and flexible energy supply. However, due to the coupling effects of electric power load demands and the complex multi-source factors within the water–mechanical–electrical system, the interrelationship between unit parameters becomes more intricate, posing significant threats to the operational reliability and health status of the units. The complexity of fault diagnosis is further aggravated by the intricate and varied nature of fault characteristics, as well as the challenges in signal extraction under conditions of strong electromagnetic interference and high noise levels. To address these issues, this paper proposes a novel data-model hybrid-driven strategy that analyzes vibration signals to achieve rapid and accurate fault diagnosis of the units. Firstly, the spectral kurtosis theory is employed to enhance the traditional empirical mode decomposition, achieving optimal decomposition and noise reduction effects for vibration signals. Secondly, the intrinsic mode functions (IMFs) obtained from the decomposition are reconstructed, and the entropy values of effective IMFs are calculated as fault feature vectors. Subsequently, the CNN-LSTM model is utilized for fault diagnosis. The effectiveness and feasibility of the proposed method are verified through actual operational data from pumped storage units in a specific region. Through analysis, the fault diagnosis accuracy of the method proposed in this paper can be maintained above 95%, demonstrating robustness in complex engineering environments and effectively ensuring the safe and stable operation of pumped storage units. Full article

The paper partially covered Active Constrained Layer Damping (ACLD) cantilever beams’ dynamic modeling, active vibration control, and parameter optimization techniques as the main topic of this research. The dynamic model of the viscoelastic sandwich beam is created by merging the finite element approach with the Golla Hughes McTavish (GHM) model. The governing equation is constructed based on Hamilton’s principle. After the joint reduction of physical space and state space, the model is modified to comply with the demands of active control. The control parameters are optimized based on the Kalman filter and genetic algorithm. The effect of various ACLD coverage architectures and excitation signals on the system’s vibration is investigated. According to the research, the genetic algorithm’s optimization iteration can quickly find the best solution while achieving accurate model tracking, increasing the effectiveness and precision of active control. The Kalman filter can effectively suppress the impact of vibration and noise exposure to random excitation on the system. Full article

This study presents an advanced data augmentation framework to enhance fault diagnostics in industrial centrifugal pumps using vibration data. The proposed framework addresses the challenge of insufficient defect data in industrial settings by integrating traditional augmentation techniques, such as Gaussian noise (GN) and signal stretching (SS), with advanced models, including Long Short-Term Memory (LSTM) networks, Autoencoders (AE), and Generative Adversarial Networks (GANs). Our approach significantly improves the robustness and accuracy of machine learning (ML) models for fault detection and classification. Key findings demonstrate a marked reduction in false positives and a substantial increase in fault detection rates, particularly in complex operational scenarios where traditional statistical methods may fall short. The experimental results underscore the effectiveness of combining these augmentation techniques, achieving up to a 30% improvement in fault detection accuracy and a 25% reduction in false positives compared to baseline models. These improvements highlight the practical value of the proposed framework in ensuring reliable operation and the predictive maintenance of centrifugal pumps in diverse industrial environments. Full article

For an electric locomotive traction motor, it is necessary to maintain relatively low vibration and noise due to the higher design standards. By using effective motor control strategies and implementing current harmonic suppression schemes, motor efficiency and vibration and noise suppression can be effectively improved. This study investigates the current harmonic suppression strategy for permanent magnet synchronous motors by (1) constructing a mathematical model of the permanent magnet motor to explore the sources of low-order harmonics currents such as fifth and seventh harmonics, as well as high-order harmonics at switch frequencies and their multiples, and analyzing the electromagnetic force characteristics generated by the current, and (2) establishing a vector control system for the permanent magnet motor. To suppress the fifth and seventh harmonic components in the current, a resonance controller is constructed, which utilizes the parallel connection of a resonator and PI controller to achieve low-order harmonic suppression. The factors affecting the effectiveness of the resonance controller’s suppression are also analyzed. The experiments are conducted, and the current harmonic suppression scheme constructed in this study can effectively reduce the harmonics in the current, thereby reducing motor vibration and noise. Full article

This paper endeavors to tackle the issue of horizontal vibrations encountered in high-speed and ultra-high-speed elevator cabins during operation. Given the limitations of traditional passive-control guide shoes in effectively mitigating these vibrations and the complexity and cost associated with active control systems, a novel approach involving passive-control rolling guide shoes (PCRGS) integrated with hydraulic damping is explored. The PCRGS incorporates a hydraulic actuator and hydraulic damping, which can be modeled by a mechanical and hydraulic co-simulation model using AMESim2020 software. The simulation reveals a substantial reduction in cabin vibrations equipped with PCRGS. Specifically, under pulse excitation, the reduction ranges from 26.2% to 27.5%; under white noise excitation, it varies between 14.3% and 17.1%; and under sine wave excitation, the reduction spans 21.2% to 24.1%. Notably, the system meets the stringent ‘Excellent’ (<=0.07 m/s²) performance criteria under sine wave excitation at lower frequencies, signifying its high effectiveness. These findings not only underscore the potential of hydraulic passive-control guide shoes in mitigating elevator vibrations but also provide invaluable guidance for their further development and refinement. Full article