Bearing Fault Detection and Classification Based on Temporal Convolutions and LSTM Network in Induction Machine

Document Type : Research Article

Authors

1 Department of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran

2 Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran

Abstract

One of the critical components in most electromechanical systems are the bearing system. Therefore, a proper condition monitoring method that can classify the type and the severity of electrical machine faults in different load levels is crucial to avoid unwanted downtime and loss of operation. Non-invasive condition monitoring methods based on electrical signatures of machine in an electromechanical system, are considered as simple and cost-effective approaches for the fault detection process. In this paper, a deep learning approach based on a combination of temporal convolutions and Long Short Term Memory (LSTM) network is used for fault diagnosis. The two architectures are both shown to be effective for time-series classification and sequence modeling. Temporal convolutions are shown to be competent in feature extraction for time-series classification; however, they are rarely studied in bearing fault detection and classification in an electromechanical system. The presented method does not need any preprocessing or predetermined signal transformation, and uses the raw time-series sensor data. In this regard, three different faults, as inner race, outer race, and balls are considered for validity of the proposed method. The results show that healthy cases can be separated from faulty cases in different load levels with high accuracy (95.8%).

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Main Subjects

References

  1. Ojaghi, M. Sabouri and J. Faiz, “Analytic Model for Induction Motors Under Localized Bearing Faults,” IEEE Transactions on Energy Conversion, vol. 33, no. 2, pp. 617-626, June 2018.
  2. Xin Zhang, Zhiwen Liu, Jiaxu Wang, Jinglin Wang, “Time–frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor wavelets,” ISA Transactions, Volume 87, pp. 225-234, 2019,
  3. Zhibin Zhao, Tianfu Li, Jingyao Wu, Chuang Sun, Shibin Wang, Ruqiang Yan, Xuefeng Chen, “Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study,” ISA Transactions, Volume 107, pp. 224-255, 2020.
  4. Yang, N. Chai, Z. Liu, B. Ren and D. Xu, "Motor Speed Signature Analysis for Local Bearing Fault Detection With Noise Cancellation Based on Improved Drive Algorithm,"  IEEE Transactions on Industrial Electronics, vol. 67, no. 5, pp. 4172-4182, May 2020.
  5. Zhao, D. Wang, R. Yan, K. Mao, F. Shen and J. Wang, "Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks," IEEE Transactions on Industrial Electronics, vol. 65, no. 2, pp. 1539-1548, Feb. 2018.
  6. You, C. Shen, D. Wang, L. Chen, X. Jiang and Z. Zhu, "An Intelligent Deep Feature Learning Method With Improved Activation Functions for Machine Fault Diagnosis," IEEE Access, vol. 8, pp. 1975-1985, 2020.
  7. Zhang, S. Zhang, B. Wang, T.G. Habetler. “Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review,” IEEE Access, vol. 8, pp.29857-29881, Feb. 2020.
  8. Chen, C. Li, R.V. Sanchez, “Gearbox fault identification and classification with convolutional neural networks,” Shock and Vibration, 2015.
  9. Tao, T. Zhang, J. Yang, X. Wang, W. Lu, “Bearing Fault Diagnosis Method bBased on Stacked Autoencoder and Softmax Regression,” 34th Chinese Control Conference (CCC), IEEE, pp. 6331-6335, Jul. 2015.
  10. Chen, W. Li, “Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network,” IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 7, pp. 1693-1702, Mar. 2017.
  11. Abed, S. Sharma, R. Sutton, A. Motwani, “A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under non-Stationary Operating Conditions,” Journal of Control, Automation and Electrical Systems, vol. 26, no. 3, pp. 241-254, Jun. 2015.
  12. Mao, Y. Liu, L. Ding, y. Li, “Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study,” IEEE Access, vol. 7, pp. 9515-9530, Jan. 2019.
  13. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, R.X. Gao, “Deep Learning and its Applications to Machine Health Monitoring,” Mechanical Systems and Signal Processing, pp.213-237. Jan. 2019.
  14. Karim, S. Majumdar, H. Darabi and S. Chen, “LSTM Fully Convolutional Networks for Time Series Classification,” IEEE Access, vol. 6, pp. pp. 1662-1669, 2018.
  15. Lea, R. Vidal, A. Reiter, and G. D. Hager, “Temporal Convolutional Networks: A Unified Approach Tto Action Segmentation,” European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, pp. 47–54, Oct. 2016.
  16. Lu, Z. Wang, B. Zhou, “ Intelligent Fault Diagnosis of Rolling Bearing using Hierarchical Convolutional Network Based Health State Classification,” Advanced Engineering Informatics, pp. 139-151, Apr. 2017.
  17. Wen, X. Li, L. Gao, 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, Nov. 2017.
  18. Zhang, C. Li, G. Peng, Y. Chen, Z. Zhang, “A Deep Convolutional Neural Network with New Training Methods For Bearing Fault Diagnosis Under Noisy Environment and Different Working Load,” Mechanical Systems and Signal Processing, pp. 439-453, Feb. 2018.
  19. Zhang, F. Zhang, W. Chen, Y. Jiang, D. Song, “Fault State Recognition of Rolling Bearing Based Fully Convolutional Network,” Computing in Science & Engineering. Vol. 21, no. 5, pp. 55-63, Jan. 2018.
  20. Qian, S. Li, J. Wang, Z. An, X. Jiang, “An Intelligent Fault Diagnosis Framework for Raw Vibration Signals: Adaptive Overlapping Convolutional Neural Network,” Measurement Science and Technology, vol. 29, no. 9, p. 095009, Aug. 2018.
  21. Eren, T. Ince, S. Kiranyaz, “A Generic Intelligent Bearing Fault Diagnosis System Uusing Compact Adaptive 1D CNN Classifier,” Journal of Signal Processing Systems, vol. 91, no. 2, pp. 179-189, Feb. 2019.
  22. Pan, X. He, S. Tang, F. Meng, “An Improved Bearing Fault Diagnosis Method using One-Dimensional CNN and LSTM,” J. Mech. Eng. vol. 64, no. 7-8, pp. 443-452 May. 2018.
  23. Yu, J. Qu, F. Gao, Y. Tian, “A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM,” Shock and Vibration, 2019.
  24. Blodt, P. Granjon, B. Raison, and G. Rostaing, “Models for Bearing Damage Detection  in  Induction Motors Using Stator Current Monitoring,”  IEEE  Transaction on  Industrial  Electronics,  vol. 55, no.  4, pp. 1813–1822, Apr. 2008.
  25. Zaremba, I. Sutskever, and O. Vinyals, “Recurrent Neural Network Regularization,” arXiv preprint arXiv:1409.2329, 2014.
  26. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
  27. Bahdanau, K. Cho, and Y. Bengio, ‘‘Neural Machine Translation by Jointly Learning to Align and Translate,’’ arXiv preprint arXiv: 1409.0473, 2019. [Online]. Available: https://arxiv.org/abs/1409.0473.
  28. Wang, W. Yan and T. Oates, "Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline," 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, pp. 1578-1585, 2017.
  29. Steinarsson, "Downsampling Time Series for Visual Representation," PhD diss., 2013.
  30. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, ... & M. Kudlur, “TensorFlow: A System for Large-Scale Machine Learning,” in Proc. OSDI, Savannah, GA, USA, pp. 265-284, Nov. 2016.
  31. J. Hand and R. J. Till, “A Simple Generalisation of The Area Under The ROC Curve For Multiple Class Classification Problems,” Machine Learning, vol. 45, no. 2, pp. 171–186, 2001.
  32. V. D. Maaten and G. Hinton, “Visualizing Data using t-SNE,” Journal of Machine Learning Research , vol. 9, pp. 2579-2605, Nov. 2008.
AUT Journal of Electrical Engineering
Volume 54, Issue 1
June 2022
Pages 107-120

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