Authors: Cerrada, Mariela | Sánchez, René-Vinicio | Cabrera, Diego
Article Type: Research Article
Abstract: Fault diagnosis plays a crucial role to maintain healthy conditions in rotating machinery. In real industrial applications, a Machine Learning based Classifier (ML-C) analyses data from a current machinery condition to detect abnormal behaviours. Usually, this is achieved through a previous training of the ML-C model, under supervised learning; however, for new machinery conditions, the classifier is not able to correctly identify these new condition. This paper proposes a framework to detect new patterns of abnormal conditions in gearboxes, that could be associated to new faults. The framework relies on an algorithm to build evolving models in simultaneous scenarios of …classification and clustering. The design is inspired by the main principles of the K-means and the One Nearest Neighbour (1-NN) algorithms. A heuristic metric is defined to analyse the new discovered clusters; as a result, these new clusters can be labelled as new classes corresponding to new faulty patterns. Once a new pattern is identified, the associated data feeds a dedicated supervised classifier which is updated through a new training phase. The proposed framework is tested on data collected from a gearbox test bed under realistic conditions of faults. Experimental results show that the algorithm is able to discover new valuable knowledge than can be identified as new faulty classes. Show more
Keywords: Knowledge discovery, machine learning, semi-supervised learning, fault detection, fault diagnosis, gearboxes
DOI: 10.3233/JIFS-169535
Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3581-3593, 2018
Authors: Sánchez, René-Vinicio | Lucero, Pablo | Vásquez, Rafael E. | Cerrada, Mariela | Macancela, Jean-Carlo | Cabrera, Diego
Article Type: Research Article
Abstract: Gearboxes and bearings play an important role in industries for motion and torque transmission machines. Therefore, early diagnoses are sought to avoid unplanned shutdowns, catastrophic damage to the machine or human losses; additionally, an appropriate diagnosis contributes to increase productivity and reduce maintenance costs. This paper addresses a methodological framework for the diagnosis of multi-faults in rotating machinery through the use of features rankings. The classification uses K nearest neighbors and random forest, based on the information that comes from the measured vibration signal. Thirty features in time domain are calculated from the vibration signal, twenty-four features commonly used in …fault diagnosis in rotating machinery, and six features are used from the field of electromyography. Feature ranking methods such as ReliefF algorithm, Chi-Square, and Information Gain are used to select the ten most relevant features, the same ones that enter the classifiers. Five databases were used to validate the proposed methodological framework. The results show good accuracy in classification for the five databases; furthermore, in all the databases in the first ten features ranked by the three rankings methods are present at least two nonconventional features. Show more
Keywords: Feature ranking, multi-fault diagnosis, rotating machinery, time features
DOI: 10.3233/JIFS-169526
Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3463-3473, 2018
Echo state network and variational autoencoder for efficient one-class learning on dynamical systems
Authors: Cabrera, Diego | Sancho, Fernando | Cerrada, Mariela | Sánchez, René-Vinicio | Tobar, Felipe
Article Type: Research Article
Abstract: Usually, time series acquired from some measurement in a dynamical system are the main source of information about its internal structure and complex behavior. In this situation, trying to predict a future state or to classify internal features in the system becomes a challenging task that requires adequate conceptual and computational tools as well as appropriate datasets. A specially difficult case can be found in the problems framed under one-class learning. In an attempt to sidestep this issue, we present a machine learning methodology based in Reservoir Computing and Variational Inference. In our setting, the dynamical system generating the time …series is modeled by an Echo State Network (ESN), and the parameters of the ESN are defined by an expressive probability distribution which is represented as a Variational Autoencoder. As a proof of its applicability, we show some results obtained in the context of condition-based maintenance in rotating machinery, where vibration signals can be measured from the system, our goal is fault detection in helical gearboxes under realistic operating conditions. The results show that our model is able, after trained only with healthy conditions, to discriminate successfully between healthy and faulty conditions and overcome other classical methodologies. Show more
Keywords: Dynamical system modeling, deep learning, reservoir computing, variational inference
DOI: 10.3233/JIFS-169552
Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3799-3809, 2018
Authors: Li, Chuan | Valente de Oliveira, José | Sanchez, René-Vinicio | Cerrada, Mariela | Zurita, Grover | Cabrera, Diego
Article Type: Research Article
Abstract: Detecting early faults in rolling element bearings is a crucial measure for the health maintenance of rotating machinery. As faulty features of bearings are usually demodulated into a high-frequency band, determining the informative frequency band (IFB) from the vibratory signal is a challenging task for weak fault detection. Existing approaches for IFB determination often divide the frequency spectrum of the signal into even partitions, one of which is regarded as the IFB by an individual selector. This work proposes a fuzzy technique to select the IFB with improvements in two aspects. On the one hand, an IFB-specific fuzzy clustering method …is developed to segment the frequency spectrum into meaningful sub-bands. Considering the shortcomings of the individual selectors, on the other hand, three commonly-used selectors are combined using a fuzzy comprehensive evaluation method to guide the clustering. Among all the meaningful sub-bands, the one with the minimum comprehensive cost is determined as the IFB. The bearing faults, if any, can be detected from the demodulated envelope spectrum of the IFB. The proposed fuzzy technique was evaluated using both simulated and experimental data, and then compared with the state-of-the-art peer method. The results indicate that the proposed fuzzy technique is capable of generating a better IFB, and is suitable for detecting bearing faults. Show more
Keywords: Rolling element bearing, fuzzy clustering, fuzzy comprehensive evaluation, fault detection, envelope demodulation
DOI: 10.3233/IFS-162097
Citation: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 6, pp. 3513-3525, 2016
Authors: Chen, Zhi-Qiang | Wang, Rong-Long | Sanchez, René-Vinicio | de Oliveira, José V. | Li, Chuan
Article Type: Research Article
Abstract: Continuous function optimization is ubiquitous in many branches of Science and Technology. Memetic algorithms are a particularly interesting approach to the optimization of continuous, non-linear, multimodal, ill-conditioned or noisy functions as these algorithms do not require derivatives and balance global exploratory search with local refinement. The Wang genetic algorithm promotes genetic diversity (exploratory capacities) by applying crossover only to parents with sufficient different chromosomes (genomes). In this work an improvement of the Wang algorithm is proposed that allows for an adaptive evaluation of the genomic difference between individuals in a way that is independent of the optimization problem and takes …into account the stage of the evolutionary process. Moreover, the work proposes an original and relevant memetic algorithm combining the improved Wang genetic algorithm, for exploration purposes, with the covariance matrix adaptation evolutionary strategy (CMA-ES) for refinements. The proposed algorithm is empirically evaluated using 25 bench marking functions against five state-of-the-art memetic algorithms revealing superior performance which is a strong evidence on the relevance of proposed algorithm. Show more
Keywords: Memetic algorithms, GA, local search, continuous optimization, evolution strategies, CMA-ES, Wang algorithm
DOI: 10.3233/IDA-173402
Citation: Intelligent Data Analysis, vol. 22, no. 2, pp. 363-382, 2018
Authors: Medina, Ruben | Alvarez, Ximena | Jadán, Diana | Macancela, Jean-Carlo | Sánchez, René–Vinicio | Cerrada, Mariela
Article Type: Research Article
Abstract: Fault detection in rotating machinery is important for optimizing maintenance chores and avoiding severe damages to other parts. Signal processing based fault detection is usually performed by considering classical techniques for alternative representation of significant signals in time domain, frequency domain or time-frequency domain. An approach based on dictionary learning for sparse representations of vibration signals aiming at gearbox fault detection and classification is proposed. A gearbox signal dataset with 900 records considering the normal case and nine fault classes is analyzed. A dictionary is learned by using a training set of signals from the normal case. This dictionary is …used for obtaining the sparse representation of signals in the test set and the norm metric is used to measure the residual from the sparse representation. The extracted features are useful for machine learning based fault detection. The analysis is performed considering different load conditions. ANOVA statistical analysis shows that there are significant differences between features in the normal case and each of the faulty classes, and best ranked features form well separated clusters. An experiment of fault classification is developed using a support vector machine for multi-class classification of faults. The accuracy obtained is 95.1% in the cross-validation testing. Show more
Keywords: Dictionary learning, sparse representation, vibration signal, gearbox fault, feature extraction
DOI: 10.3233/JIFS-169537
Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3605-3618, 2018
Authors: Peña, Mario | Cerrada, Mariela | Alvarez, Ximena | Jadán, Diana | Lucero, Pablo | Milton, Barragán | Guamán, Rodrigo | Sánchez, René-Vinicio
Article Type: Research Article
Abstract: The number of features for fault diagnosis in rotating machinery can be large due to the different available signals containing useful information. From an extensive set of available features, some of them are more adequate than other ones, to classify properly certain fault modes. The classic approach for feature selection aims at ranking the set of original features; nevertheless, in feature selection, it has been recognized that a set of best individually features does not necessarily lead to good classification. This paper proposes a framework for feature engineering to identify the set of features which can yield proper clusters of …data. First, the framework uses ANOVA combined with Tukey’s test for ranking the significant features individually; next, a further analysis based on inter-cluster and intra-cluster distances is accomplished to rank subsets of significant features previously identified. Our contribution aims at discovering the subset of features that discriminates better the clusters of data associated to several faulty conditions of the mechanical devices, to build more robust multi-fault classifiers. Fault severity classification in rolling bearings is studied to verify the proposed framework, with data collected from a test bed under real conditions of speed and load on the rotating device. Show more
Keywords: Feature engineering, ANOVA, cluster validity assessment, KNN, fault diagnosis, bearings
DOI: 10.3233/JIFS-169525
Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3451-3462, 2018
Authors: Li, Chuan | Cerrada, Mariela | Cabrera, Diego | Sanchez, René Vinicio | Pacheco, Fannia | Ulutagay, Gözde | Valente de Oliveira, José
Article Type: Research Article
Abstract: Bearings are one of the most omnipresent and vulnerable components in rotary machinery such as motors, generators, gearboxes, or wind turbines. The consequences of a bearing fault range from production losses to critical safety issues. To mitigate these consequences condition based maintenance is gaining momentum. This is based on a variety of fault diagnosis techniques where fuzzy clustering plays an important role as it can be used in fault detection, classification, and prognosis. A variety of clustering algorithms have been proposed and applied in this context. However, when the extensive literature on this topic is investigated, it is not clear …which clustering algorithm is the most suitable, if any. In an attempt to bridge this gap, in this study four representative fuzzy clustering algorithms are compared under the same experimental realistic conditions: fuzzy c-means (FCM), the Gustafson-Kessel algorithm, FN-DBSCAN, and FCMFP. The study considers only real-world bearing vibration data coming from both a benchmark data set (CWRU) and from a lab setup where interference between bearing faults can be studied. The comparison takes into account the quality of the generated partitions measured by the external quality (Rand and Adjusted Rand) indexes. The conclusions of the study are grounded in statistical tests of hypotheses. Show more
Keywords: Bearing, fault detection, fault diagnosis, fault classification, fuzzy rules, fuzzy clustering, FCM, Gustafson-Kessel clustering, FCMFP, FN-DBSCAN
DOI: 10.3233/JIFS-169534
Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3565-3580, 2018