Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders

Emanuele Principi; Damiano Rossetti; Stefano Squartini; Francesco Piazza

doi:10.1109/JAS.2019.1911393

Volume 6 Issue 2

Mar. 2019

IEEE/CAA Journal of Automatica Sinica

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Emanuele Principi, Damiano Rossetti, Stefano Squartini and Francesco Piazza, "Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders," IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 441-451, Mar. 2019. doi: 10.1109/JAS.2019.1911393

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Emanuele Principi, Damiano Rossetti, Stefano Squartini and Francesco Piazza, "Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders," IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 441-451, Mar. 2019. doi: 10.1109/JAS.2019.1911393

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Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders

doi: 10.1109/JAS.2019.1911393

1.
Department of Information Engineering, Università Politecnica delle Marche, Ancona 60121, Italy
2.
Loccioni Group, Angeli di Rosora, Ancona 60121, Italy

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Author Bio:
Emanuele Principi got the Italian Laurea with honors in electronic engineering from University of Ancona (now Universitá Politecnica delle Marche), Italy, in 2004. He obtained the Ph.D. degree at the same university in November 2009. He is now a Post-doctoral Researcher since March 2010. His current research interests are in the area of digital signal processing and computational intelligence, with special focus on energy management and speech/audio processing. Dr. Principi has actively participated to various (funded) regional, national and European projects on multimedia Digital Signal Processing. He is author and co-author of many international scientific peer-reviewed articles, and he has been serving as a reviewer for several international journals, and conference proceedings. He is member of the editorial board of Neural Computing and Applications and Artificial Intelligence Review both edited by Springer from 2017, and member of the program committee of several international conferences. He also served as Guest Editor for the Special Issue on "Theory and Application of Computational Intelligence in Electric Vehicles and their Integration within Smart Energy Networks" (Energies, MDPI) and he organized the Special Session on "Deep Neural Audio processing" within the IEEE International Joint Conference on Neural Networks in 2017, 2018, and 2019. He is also member of the Texas Instrument Expert Advisory Panel and of the International Neural Networks Society

Damiano Rossetti received the M.Sc. degree in electronic engineering in 2014 from Università Politecnica delle Marche (Italy). In 2014, he started his Ph.D. period at the Department of Information Engineering of Universitá Politecnica delle Marche in collaboration with the Research for Innovation Department of Loccioni Group. In 2016, he was at the University of Lincoln as visiting student for 6 months, where he carried on his research on machine learning algorithms for industrial applications. He received the Ph.D. degree in electronic engineering in 2018. He is currently a Software Designer at the Loccioni Group. (e-mail: lordrox@hotmail.it)

Stefano Squartini (SM'12) got the Italian Laurea with honours in electronic engineering from University of Ancona (now Polytechnic University of Marche, UnivPM), Italy, in 2002. He obtained the Ph.D. degree at the same university (November 2005). He worked also as post-doctoral researcher at UnivPM from June 2006 to November 2007, when he joined the DII (Department of Information Engineering) as Assistant Professor in Circuit Theory. He is now Associate Professor at UnivPM since November 2014. His current research interests are in the area of computational intelligence and digital signal processing, with special focus on speech/audio/music processing and energy management. He is author and co-author of more than 190 international scientific peer-reviewed articles. He is Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, and IEEE Transactions on Emerging Topics in Computational Intelligence, and also member of Cognitive Computation, Big Data Analytics and Artificial Intelligence Reviews Editorial Boards. He joined the Organizing and the Technical Program Committees of more than 70 International Conferences and Workshops in the recent past. He is the Organizing Chair of the IEEE CIS Task Force on Computational Audio Processing. (e-mail: s.squartini@univpm.it)

Francesco Piazza got the Italian Laurea with honours in electronic engineering from the University of Ancona, Italy, in 1981. From 1981 to 1983 he worked on image processing at the Physics Department. In 1983 he worked at the Olivetti OSAI Software Development Centre (Ivrea, Italy). In 1985 he joined the Department of Electronics and Automatics of the University of Ancona. Currently he is Full Professor of Electrical Science and Director of the Department of Information Engineering at the Universitá Politecnica delle Marche, Italy. He is author or co-author of more than 350 international papers. His current research interests are in the areas of circuit theory and digital signal processing including adaptive DSP algorithms and circuits, artificial neural networks, speech and audio processing. (e-mail:f.piazza@univpm.it)
Corresponding author: Emanuele Principi, e-mail: E.principi@univpm.it
Received Date: 2018-10-17
Revised Date: 2018-12-19
Accepted Date: 2019-01-13

Abstract

Abstract

Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literature for detecting faults automatically. Deep neural networks have been successfully employed for this task, but, up to the authors' knowledge, they have never been used in an unsupervised scenario. This paper proposes an unsupervised method for diagnosing faults of electric motors by using a novelty detection approach based on deep autoencoders. In the proposed method, vibration signals are acquired by using accelerometers and processed to extract Log-Mel coefficients as features. Autoencoders are trained by using normal data only, i.e., data that do not contain faults. Three different autoencoders architectures have been evaluated: the multi-layer perceptron (MLP) autoencoder, the convolutional neural network autoencoder, and the recurrent autoencoder composed of long short-term memory (LSTM) units. The experiments have been conducted by using a dataset created by the authors, and the proposed approaches have been compared to the one-class support vector machine (OC-SVM) algorithm. The performance has been evaluated in terms area under curve (AUC) of the receiver operating characteristic curve, and the results showed that all the autoencoder-based approaches outperform the OC-SVM algorithm. Moreover, the MLP autoencoder is the most performing architecture, achieving an AUC equal to 99.11%
- Autoencoder,
- convolutional neural networks,
- electric motor,
- fault detection,
- long short-term memory,
- neural networks,
- novelty detection

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References(55)

References

[1]	Y. Merizalde, L. Hernández-Callejo, and O. Duque-Perez, "State of the art and trends in the monitoring, detection and diagnosis of failures in electric induction motors, " Energies, vol. 10, no. 7, 2017.
[2]	X. Dai and Z. Gao, "From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis, " IEEE Transactions on Industrial Informatics, vol. 9, no. 4, pp. 2226-2238, 2013. http://ieeexplore.ieee.org/document/6423903/
[3]	Z. Gao, C. Cecati, and S. Ding, "A survey of fault diagnosis and faulttolerant techniques-part i: Fault diagnosis with model-based and signalbased approaches, " IEEE Transactions on Industrial Electronics, vol. 62, no. 6, pp. 3757-3767, 2015. http://ieeexplore.ieee.org/document/7069265/
[4]	Z. W. Gao, C. Cecati, S. X. Ding, "A survey of fault diagnosis and fault-tolerant techniques-part ii: Fault diagnosis with knowledge-based and hybrid/active approaches, " IEEE Transactions on Industrial Electronics, vol. 62, no. 6, pp. 3768-3774, 2015. http://ieeexplore.ieee.org/document/7076586/
[5]	L. Wen, X. Li, L. Gao, and Y. Zhang, "A new convolutional neural network-based data-driven fault diagnosis method, " IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5990-5998, 2018. http://ieeexplore.ieee.org/document/8114247
[6]	S. Nandi, H. A. Toliyat, and X. Li, "Condition monitoring and fault diagnosis of electrical motors -a review, " IEEE Transactions on Energy Conversion, vol. 20, no. 4, 2005. http://www.emeraldinsight.com/servlet/linkout?suffix=b15&dbid=16&doi=10.1108%2F03321641111101140&key=10.1109%2FTEC.2005.847955
[7]	M. Seera, C. P. Lim, S. Nahavandi, and C. K. Loo, "Condition monitoring of induction motors: A review and an application of an ensemble of hybrid intelligent models, " Expert Systems with Applications, vol. 41, no. 10, pp. 4891-4903, 2014. doi: 10.1016/j.eswa.2014.02.028
[8]	R. Isermann, "Model-based fault-detection and diagnosis -status and applications, " Annual Reviews in Control, vol. 29, no. 1, pp. 71-85, 2005. doi: 10.1016/j.arcontrol.2004.12.002
[9]	M. Bouzid and G. Champenois, "New expression of symmetrical components of the inductor motor under stator faults, " IEEE Trans. Ind. Electron., vol. 60, no. 9, pp. 4093-4410, 2013. doi: 10.1109/TIE.2012.2235392
[10]	A. Adouni, A. Abid, and L. Sbita, "A DC motor fault detection, isolation and identification based on a new architecture Artificial Neural Network, " in Proc. 5th Int. Conf. on Systems and Control (ICSC), 2016, pp. 294-299. https://ieeexplore.ieee.org/document/7507054
[11]	P. Konar and P. Chattopadhyay, "Bearing fault detection of induction motor using wavelet and support vector machines (SVMs), " Applied Soft Computing Journal, vol. 11, no. 6, pp. 4203-4211, 2011. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0d0683a77de2881328a87d52b88dabe9
[12]	V. N. Ghate and S. V. Dudul, "Cascade neural-network-based fault classifier for three-phase induction motor, " IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 1555-1563, 2011. doi: 10.1109/TIE.2010.2053337
[13]	R. Razavi-Far, M. Farajzadeh-Zanjani, S. Zare, M. Saif, and J. Zarei, "One-class classifiers for detecting faults in induction motors, " in Proc. 30th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2017. http://ieeexplore.ieee.org/document/7946719/
[14]	A. Soualhi, G. Clerc, and H. Razik, "Detection and diagnosis of faults in induction motor using an improved artificial ant clustering technique, " IEEE Transactions on Industrial Electronics, vol. 60, no. 9, pp. 4053-4062, 2013. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=6ee5677300154bed234412c0e1d776f7
[15]	H. C. Cho, J. Knowles, S. Fadali, and K. S. Lee, "Fault detection and isolation of induction motors using recurrent neural networks and dynamic Bayesian modeling, " IEEE Transactions on Control Systems Technology, vol. 18, no. 2, pp. 430-437, 2010. doi: 10.1109/TCST.2009.2020863
[16]	M. Markou and S. Singh, "Novelty detection: A review -part 1: Statistical approaches, " Signal Processing, vol. 83, no. 12, pp. 2481-2497, 2003. doi: 10.1016/j.sigpro.2003.07.018
[17]	M. Markou and S. Singh, "Novelty detection: A review -part 2: Neural network based approaches, " Signal Processing, vol. 83, no. 12, pp. 2499-2521, 2003. doi: 10.1016/j.sigpro.2003.07.019
[18]	E. Principi, F. Vesperini, S. Squartini, and F. Piazza, "Acoustic novelty detection with adversarial autoencoders, " in Proc. Int. Joint Conf. Neural Networks (IJCNN), Anchorage, AK, USA, 2017, pp. 3324-3330. http://ieeexplore.ieee.org/document/7966273/
[19]	T. Schlegl, P. Seeböck, S. Waldstein, U. Schmidt-Erfurth, and G. Langs, "Unsupervised anomaly detection with generative adversarial networks to guide marker discovery, " Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10265 LNCS, pp. 146-147, 2017. doi: 10.1007/978-3-319-59050-9_12
[20]	P. García-Teodoro, J. Díaz-Verdejo, G. Maciá-Fernáandez, and E. Váazquez, "Anomaly-based network intrusion detection: Techniques, systems and challenges, " Computers and Security, vol. 28, no. 1-2, pp. 18-28, 2009. doi: 10.1016/j.cose.2008.08.003
[21]	C. Gong, "Exploring commonality and individuality for multi-modal curriculum learning, " in Proc. 31st AAAI Conference on Artificial Intelligence, 2017, pp. 1926-1933. http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14205
[22]	C. Gong, D. Tao, S. Maybank, W. Liu, G. Kang, and J. Yang, "Multimodal curriculum learning for semi-supervised image classification, " IEEE Transactions on Image Processing, vol. 25, no. 7, pp. 3249-3260, 2016. http://ieeexplore.ieee.org/document/7465792/
[23]	C. Gong, D. Tao, W. Liu, L. Liu, and J. Yang, "Label propagation via teaching-to-learn and learning-to-teach, " IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 6, pp. 1452-1465, 2017. doi: 10.1109/TNNLS.2016.2514360
[24]	C. Gong, T. Liu, D. Tao, K. Fu, E. Tu, and J. Yang, "Deformed graph laplacian for semisupervised learning, " IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 10, pp. 2261-2274, 2015. doi: 10.1109/TNNLS.2014.2376936
[25]	F. Jia, Y. Lei, J. Lin, X. Zhou, and N. Lu, "Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, " Mechanical Systems and Signal Processing, vol. 72-73, pp. 303-315, 2016. doi: 10.1016/j.ymssp.2015.10.025
[26]	T. Ince, S. Kiranyaz, L. Eren, M. Askar, and M. Gabbouj, "Real-time motor fault detection by 1-D convolutional neural networks, " IEEE Transactions on Industrial Electronics, vol. 63, no. 11, pp. 7067-7075, 2016. doi: 10.1109/TIE.2016.2582729
[27]	W. Zhang, G. Peng, C. Li, Y. Chen, and Z. Zhang, "A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals, " Sensors, vol. 17, no. 2, 2017. http://europepmc.org/articles/PMC5336047/
[28]	Y. LeCun. LeNet-5, convolutional neural networks[Online]. Available: http://yann.lecun.com/exdb/lenet, 2015.
[29]	S. Zgarni, H. Keskes, and A. Braham, "Nested SVDD in DAG SVM for induction motor condition monitoring, " Engineering Applications of Artificial Intelligence, vol. 71, pp. 210-215, 2018. doi: 10.1016/j.engappai.2018.02.019
[30]	J. Sun, C. Yan, and J. Wen, "Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning, " IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 1, pp. 185-195, 2018. doi: 10.1109/TIM.2017.2759418
[31]	F. Jia, Y. Lei, L. Guo, J. Lin, and S. Xing, "A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines, " Neurocomputing, vol. 272, pp. 619-628, 2018. doi: 10.1016/j.neucom.2017.07.032
[32]	H. Shao, H. Jiang, Y. Lin, and X. Li, "A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders, " Mechanical Systems and Signal Processing, vol. 102, pp. 278-297, 2018. doi: 10.1016/j.ymssp.2017.09.026
[33]	B. Schölkopf, R. C. Williamson, A. J. Smola, J. Shawe-Taylor, and J. C. Platt, "Support vector method for novelty detection, " in Advances in Neural Information Processing Systems, vol. 12. MIT Press, 2000, pp. 582-588. http://dl.acm.org/citation.cfm?id=3009740
[34]	I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 1st ed. Cambridge, Massachussets, USA: MIT Press, 2016, ch. 14, pp. 502-524.
[35]	M. Seltzer, D. Yu, and Y. Wang, "An investigation of deep neural networks for noise robust speech recognition, " in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, Canada, 2013, pp. 7398-7402. https://ieeexplore.ieee.org/document/6639100
[36]	A.-R. Mohamed, G. Hinton, and G. Penn, "Understanding how deep belief networks perform acoustic modelling, " in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, 2012, pp. 4273-4276. http://ieeexplore.ieee.org/document/6288863/
[37]	Y. Kashimoto, M. Fujimoto, H. Suwa, Y. Arakawa, and K. Yasumoto, "Floor vibration type estimation with piezo sensor toward indoor positioning system, " in Proc. Int. Conf. on Indoor Positioning and Indoor Navigation, Madrid, Spain, 2016, pp. 1-6. http://ieeexplore.ieee.org/document/7743667/
[38]	F. Nelwamondo and T. Marwala, "Faults detection using gaussian mixture models, mel-frequency cepstral coefficients and kurtosis, " in Proc. IEEE Int. Conf. on Systems, Man and Cybernetics, vol. 1, Taipei, China, 2007, pp. 290-295. http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=4273844
[39]	A. Oppenheim and R. Schafer, Discrete-time Signal Processing, 3rd ed. Englewood Cliffs, NJ, USA: Prentice-Hall, Inc., 2009.
[40]	S. Davis and P. Mermelstein, "Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences, " IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 28, no. 4, pp. 357-366, 1980. doi: 10.1109/TASSP.1980.1163420
[41]	D. O'Shaughnessy, Speech Communications: Human and Machine, 2nd ed. IEEE, 1999.
[42]	S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift, " in Proc. 32nd International Conference on Machine Learning (ICML), vol. 1, 2015, pp. 448-456. http://www.arxiv.org/abs/1502.03167
[43]	I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.
[44]	S. Hochreiter and J. Schmidhuber, "Long short-term memory, " Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997. doi: 10.1162/neco.1997.9.8.1735
[45]	S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, 2004.
[46]	D. Kingma and J. Ba, "Adam: A method for stochastic optimization, " arXiv preprint arXiv: 1412.6980, 2014. http://www.oalib.com/paper/4068193
[47]	J. Bergstra and Y. Bengio, "Random search for hyper-parameter optimization, " Journal of Machine Learning Research, vol. 13, pp. 281-305, 2012. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=CC0214989014
[48]	M. Kliger and S. Fleishman, "Novelty detection with gan, " arXiv: 1802.10560, 2018. https://arxiv.org/abs/1802.10560
[49]	D. Droghini, F. Vesperini, E. Principi, S. Squartini, and F. Piazza, "Fewshot siamese neural networks employing audio features for humanfall detection, " in Proc. International Conference on Pattern Recognition and Artificial Intelligence, Union, NJ, USA, 2018, pp. 63-69.
[50]	G. Koch, R. Zemel, and R. Salakhutdinov, "Siamese neural networks for one-shot image recognition, " in ICML Deep Learning Workshop, vol. 2, 2015.
[51]	U. Orji, Z. Remscrim, C. Laughman, S. Leeb, W. Wichakool, C. Schantz, R. Cox, J. Paris, J. Kirtley Jr., and L. Norford, "Fault detection and diagnostics for non-intrusive monitoring using motor harmonics, " in Proc. IEEE Applied Power Electronics Conference and Exposition (APEC), Palm Springs, CA, USA, 2010, pp. 1547-1554. http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5433437
[52]	R. Bonfigli, E. Principi, M. Fagiani, M. Severini, S. Squartini, and F. Piazza, "Non-intrusive load monitoring by using active and reactive power in additive factorial hidden markov models, " Applied Energy, vol. 208, pp. 1590-1607, 2017. doi: 10.1016/j.apenergy.2017.08.203
[53]	A. Graves and N. Jaitly, "Towards end-to-end speech recognition with recurrent neural networks, " in Proc. International Conference on Machine Learning (ICML), vol. 5, 2014, pp. 3771-3779.
[54]	W. Caesarendra and T. Tjahjowidodo, "A review of feature extraction methods in vibration-based condition monitoring and its application for degradation trend estimation of low-speed slew bearing, " Machines, vol. 5, no. 4, 2017.
[55]	Z. Li, Y. Jiang, Q. Guo, C. Hu, and Z. Peng, "Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations, " Renewable Energy, vol. 116, pp. 55-73, 2018. doi: 10.1016/j.renene.2016.12.013

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Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders

doi: 10.1109/JAS.2019.1911393

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