Simulation-driven Bearing Fault Diagnosis for Condition Monitoring without Faulty Data

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Published Sep 4, 2023
Iljeok Kim Seungchul Lee

Abstract

The failure of rolling element bearings in complex mechanical systems is a significant cause of mechanical failures, leading to decreased productivity and safety risks. Deep learning has shown promising results in bearing fault diagnosis, but the predictive performance depends on highquality data. Domain adaptation has been studied to solve this problem, but it still has limitations when applied to real-world industrial applications. In this study, we propose a deep learning-based domain generalization framework for bearing fault diagnosis using the bearing simulation model and adversarial data augmentation method. The proposed framework was validated on a real bearing fault dataset and showed promising results in improving diagnostic performance in cases where fault data cannot be obtained or when dealing with unlearned target domains. This approach has the potential to improve industrial maintenance systems by obtaining improved generalization performance in the absence of fault datasets.

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Keywords

Bearing Fault Diagnosis, Domain Generalization, Vibration Simulation

References
D. Zhang, Y. Chen, F. Guo, G. E. Karimi, H. Dong, and Q. Xuan, "A New Interpretable Learning Method for Fault Diagnosis of Rolling Bearings," IEEE Transactions on Instrumentation and Measurement, vol. 70, p. 3507010, Dec. 2020.

H., Shao, H. Jiang, and T. Liang, "Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network," IEEE Transactions on Industrial Electronics, vol. 65, no. 3, pp. 2727-2736, Mar. 2018.

Z. Chen, and 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, Jul. 2017.

L. Zheng, Z. Wang, Z. Zhao, J. Wang, and W. Du, "Research of Bearing Fault Diagnosis Method Based on MultiLayer Extreme Learning Machine Optimized by Novel Ant Lion Algorithm," IEEE Access, vol. 7, pp. 89845- 89856, Jul. 2019.

Z. Chen, G. He, J. Li, Y. Liao, K. Gryllias, and W. Li, "Domain Adversarial Transfer Network for CrossDomain Fault Diagnosis of Rotary Machinery," IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 11, Nov. 2020.

X. Li, W. Zhang, and Q. Ding, "Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks," IEEE Transactions on Industrial Electronics, vol. 66, no. 7, Jul. 2019.

J. Antoni, “Cyclic Spectral Analysis of Rolling-element Bearing Signals: Facts and Fiction,” Journal of Sound and Vibration, vol. 304, no. 3-5, pp.497-529, Jul. 2007.

C. Liu and K. Gryllias, “Simulation-driven Domain Adaptation for Rolling Element Bearing Fault Diagnosis,” IEEE Transactions on Industrial Informations, vol. 18, pp. 5760-5770, Aug. 2021.

F. Qiao, L. Zhao, and X. Peng, “Learning to Learn Single Domain Generalization,” In Computer Vision and Pattern Recognition (CVPR), 2020.
Section
Regular Session Papers