research-article
Authors: Iljeok Kim, Sung Wook Kim, Jeongsan Kim, Hyunsuk Huh, + 4, Iljoo Jeong, Taegyu Choi, Jeongchan Kim, and Seungchul Lee (Less)
Volume 241, Issue C
Published: 25 June 2024 Publication History
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Abstract
State-of-the-art deep learning methods have demonstrated impressive performance in the intelligent fault diagnosis of rolling element bearings. However, in previous studies, critical issues such as domain discrepancy and the inability to interpret a classification decision made it difficult to apply deep learning in real industrial scenarios. Although domain adaptation and domain generalization-based methods have been investigated to solve domain discrepancy, collecting labeled data for various domains (especially continuous and non-stationary working conditions) is extremely difficult in an engineering application. Furthermore, since the classification decision cannot be physically explained, serious reliability problems arise for unseen working conditions (i.e., target domain with domain discrepancy). This study proposes the single domain generalizable and physically interpretable (SDGPI) framework. The proposed model embeds prior knowledge into the neural network combined with signal-preprocessing, which simultaneously enables single source domain generalization and domain interpretation with physical guarantees. Comprehensive case studies demonstrate that domain generalizable representation leads to 1) superior performance and robustness compared with existing methods for various untrained working conditions, as well as 2) efficient data inference even with small data size. Finally, the diagnosis results could be physically understood by displaying the classification decision in terms of the theoretical characteristic fault frequency (i.e., the characteristic fault order), indicating that SDGPI is a versatile and reliable diagnostic tool for unseen working conditions.
References
[1]
D. Zhang, Y. Chen, F. Guo, H.R. Karimi, H. Dong, Q. Xuan, A New Interpretable Learning Method for Fault Diagnosis of Rolling Bearings, IEEE Transactions on Instrumentation and Measurement 70 (2020).
[2]
H. Shao, H. Jiang, H. Zhang, T. Liang, Electric Locomotive Bearing Fault Diagnosis, IEEE Transactions on Industrial Electronics 65 (2018) 2727–2736.
[3]
J. Xie, G. Du, C. Shen, N. Chen, L. Chen, Z. Zhu, An end-to-end model based on improved adaptive deep belief network and its application to bearing fault diagnosis, IEEE Access 6 (2018) 63584–63596.
[4]
J. Guo, P. Zheng, A Method of Rolling Bearing Fault Diagnose Based on Double Sparse Dictionary and Deep Belief Network, IEEE Access 8 (2020) 116239–116253.
[5]
Z. Chen, W. Li, Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network, IEEE Transactions on Instrumentation and Measurement 66 (2017) 1693–1702.
[6]
F. Jia, Y. Lei, J. Lin, X. Zhou, 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 72–73 (2016) 303–315.
[7]
S.S. Udmale, S.K. Singh, Application of Spectral Kurtosis and Improved Extreme Learning Machine for Bearing Fault Classification, IEEE Transactions on Instrumentation and Measurement 68 (2019) 4222–4233.
[8]
L. Zheng, Z. Wang, Z. Zhao, J. Wang, W. Du, Research of Bearing Fault Diagnosis Method Based on Multi-Layer Extreme Learning Machine Optimized by Novel Ant Lion Algorithm, IEEE Access 7 (2019) 89845–89856.
[9]
H. Liu, J. Zhou, Y. Zheng, W. Jiang, Y. Zhang, Fault Diagnosis of Rolling Bearings with Recurrent Neural Network-based Autoencoders, ISA Transactions 77 (2018) 167–178.
[10]
M. Schuster, K.K. Paliwal, Bidirectional Recurrent Neural Networks, IEEE Transactions on Signal Processing 45 (11) (1997) 2673–2681.
Digital Library
[11]
O. Janssens, V. Slavkovikj, B. Vervisch, K. Stockman, M. Loccufier, S. Verstockt, …., S.V. Hoecke, Convolutional Neural Network Based Fault Detection for Rotating Machinery, Journal of Sound and Vibration 377 (2016) 331–345.
[12]
L.H. Wang, X.P. Zhao, J.X. Wu, Y.Y. Xie, Y.H. Zhang, Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network, Chinese Journal of Mechanical Engineering 30 (2017) 1357–1368.
[13]
Y. Cheng, M. Lin, J. Wu, H. Zhu, X. Shao, Inteligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network, Acticle 106796 Knowledge-Based Systems 216 (2021).
[14]
X. Li, W. Zhang, Q. Ding, J.Q. Sun, Multi-Layer domain adaptation method for rolling bearing fault diagnosis, Signal Processing 157 (2019) 180–197.
Digital Library
[15]
E. Tjoa, C. Guan, A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI, IEEE Transactions on Neural Networks and Learning Systems 32 (11) (2021) 4793–4813.
[16]
A. Adadi, M. Berrada, Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI), IEEE Access 6 (2018) 52138–52160.
[17]
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018). Explaining Explanations: An Overview of Interpretability of Machine Learning, The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018), 80-89.
[18]
J. Grezmak, J. Zhang, P. Wang, K.A. Loparo, R.X. Gao, Interpretable Convolutional Neural Network Through Layer-wise Relevance Propagation for Machine Fault Diagnosis, IEEE Sensors Journal 20 (6) (2020) 3172–3181.
[19]
Z. Chen, G. He, J. Li, Y. Liao, K. Gryllias, W. Li, Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery, IEEE Transactions on Instrumentation and Measurement 69 (11) (2020) 8702–8712.
[20]
X. Li, W. Zhang, Q. Ding, Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks, IEEE Transactions on Industrial Electronics 66 (7) (2019) 5525–5534.
[21]
W. Zhang, G. Peng, C. Li, Y. Chen, Z. Zhang, A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals, Sensors 17 (2) (2017) Article 425.
[22]
W. 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 100 (2018) 439–453.
[23]
G. Jiang, H. He, J. Yan, P. Xie, Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox, IEEE Transactions on Industrial Electronics 66 (4) (2019) 3196–3207.
[24]
T. Han, Y.F. Li, M. Qian, A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions, IEEE Transactions on Instrumentation and Measurement 70 (2021) Article 3520011.
[25]
Qiao, F., Zhao, L., & Peng, X. (2020). Learning to Learn Signal Domain Generalization. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26]
Li, L., Gao, K., Cao, J., Huang, Z., Weng, Y., Mi, X., Yu, Z., Li, X., & Xia, B. (2021). Progressive Domain Expansion Network for Single Domain Generalization, In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27]
H. Wang, Z. Liu, D. Pend, Y. Qin, Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis, IEEE Transactions on Industrial Informatics 16 (9) (2020) 5735–5745.
[28]
M.S. Kim, J.P. Yun, P.G. Park, An explainable convolutional neural network for fault diagnosis in linear motion guide, IEEE Transactions on Industrial Informatics 17 (6) (2021) 4036–4045.
[29]
H.Y. Chen, C.H. Lee, Vibration signals analysis by explainable artificial intelligence (XAI) approach: Application on bearing faults diagnosis, IEEE Access 8 (2020) 134246–134256.
[30]
Muandet, K., Balduzzi, D., & Schölkopf, B. (2013). Domain Generalization via Invariant Feature Representation. In Proceedings of the 30th International Conference on Machine Learning (ICML), 28, 10-18.
[31]
A. Klausen, H.V. Khang, K.G. Robbersmyr, Multi-band identification for enhancing bearing fault detection in variable speed conditions, Mechanical Systems and Signal Processing 139 (2020).
[32]
G. Montavon, S. Lapuschkin, A. Binder, W. Samek, K.R. Müller, Explaining nonlinear classification decisions with deep Taylor decomposition, Pattern Recognition 65 (2017) 211–222.
[33]
Masters, D., & Luschi, C. (2018). Revisiting Small Batch Training for Deep Neural Networks. arXiv preprint arXiv:1804.07612.
[34]
N.S. Keskar, D. Mudigere, J. Nocedal, M. Smelyanskiy, P.T.P. Tang, On large-batch training for deep learning: generalization gap and sharp minima, The International Conference on Learning Representations (ICLR), 2017.
[35]
I. Goodfellow, Y. Benjio, A. Courville, Deep learning, The MIT Press, Cambridge, MA, 2016.
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Published In
Expert Systems with Applications: An International Journal Volume 241, Issue C
May 2024
1588 pages
ISSN:0957-4174
Issue’s Table of Contents
Elsevier Ltd.
Publisher
Pergamon Press, Inc.
United States
Publication History
Published: 25 June 2024
Author Tags
- Cross-domain bearing fault diagnosis
- Single domain generalization
- Signal processing
- explainable AI
- Domain-invariant representation
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