A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 6 Issue 2
Mar.  2019

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
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
Citation: 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

Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders

doi: 10.1109/JAS.2019.1911393
More Information
  • 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%


  • loading
  • [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.
    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/
    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/
    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/
    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
    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
    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
    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
    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
    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
    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
    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
    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/
    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
    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
    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
    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
    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/
    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
    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
    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
    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/
    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
    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
    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
    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
    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/
    Y. LeCun. LeNet-5, convolutional neural networks[Online]. Available: http://yann.lecun.com/exdb/lenet, 2015.
    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
    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
    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
    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
    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
    I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 1st ed. Cambridge, Massachussets, USA: MIT Press, 2016, ch. 14, pp. 502-524.
    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
    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/
    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/
    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
    A. Oppenheim and R. Schafer, Discrete-time Signal Processing, 3rd ed. Englewood Cliffs, NJ, USA: Prentice-Hall, Inc., 2009.
    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
    D. O'Shaughnessy, Speech Communications: Human and Machine, 2nd ed. IEEE, 1999.
    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
    I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.
    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
    S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, 2004.
    D. Kingma and J. Ba, "Adam: A method for stochastic optimization, " arXiv preprint arXiv: 1412.6980, 2014. http://www.oalib.com/paper/4068193
    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
    M. Kliger and S. Fleishman, "Novelty detection with gan, " arXiv: 1802.10560, 2018. https://arxiv.org/abs/1802.10560
    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.
    G. Koch, R. Zemel, and R. Salakhutdinov, "Siamese neural networks for one-shot image recognition, " in ICML Deep Learning Workshop, vol. 2, 2015.
    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
    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
    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.
    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.
    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


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(10)  / Tables(2)

    Article Metrics

    Article views (1781) PDF downloads(103) Cited by()


    DownLoad:  Full-Size Img  PowerPoint