Bearing Fault Diagnosis Based on Ensemble Depth Explainable Encoder Classification Model with Arithmetic Optimized Tuning
by Kaibi Zhang, Yanyan Wang, Hongchun Qu* 
Chongqing University of Posts and Telecommunications, College of Automation, Chongqing, 400065, China
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 3, Page # 81-97, 2022; DOI: 10.55708/js0103009
Keywords: Fault diagnosis, Ensemble model, Dynamic composition, Deep auto-encoder, Layer-wise Relevance Propagation
Received: 10 January 2022, Revised: 19 February 2022, Accepted: 05 March 2022, Published Online: 17 March 2022
AMA Style
Zhang K, Wang Y, Qu H. Bearing fault diagnosis based on ensemble depth explainable encoder classification model with arithmetic optimized tuning. Journal of Engineering Research and Sciences. 2022;1(3):81-97. doi:10.55708/js0103009
Chicago/Turabian Style
Zhang, Kaibi, Yanyan Wang, and Hongchun Qu. “Bearing Fault Diagnosis Based on Ensemble Depth Explainable Encoder Classification Model with Arithmetic Optimized Tuning.” Journal of Engineering Research and Sciences 1, no. 3 (2022): 81–97. https://doi.org/10.55708/js0103009.
IEEE Style
K. Zhang, Y. Wang, and H. Qu, “Bearing fault diagnosis based on ensemble depth explainable encoder classification model with arithmetic optimized tuning,” Journal of Engineering Research and Sciences, vol. 1, no. 3, pp. 81–97, 2022.
In a dynamic and complex bearing operating environment, current auto-encoder-based deep models for fault diagnosis are having difficulties in adaptation, which usually leads to a decline in accuracy. Besides, the opaqueness of the decision process by such deep models might reduce the reliability of the diagnostic results, which is not conducive to the subsequent optimization of the model. In this work, an ensemble deep auto-encoder method is developed and tested for intelligent fault diagnosis. To mitigate the influence of the changing operating environment on the diagnostic accuracy of the model, a tuning algorithm is used to adaptively adjust the parameters of the model, and a hypersphere classification algorithm is used to separately train different types of fault data. The encoder components in the ensemble model are automatically updated based on the diagnostic accuracy of the base encoder model under different operating conditions. To improve the reliability of the diagnosis results, the power spectrum analysis and Layer-wise Relevance Propagation algorithm are combined to explain the diagnosis results. The model was validated on three public datasets and compared with individual encoder methods as well as other common fault diagnosis algorithms. The results confirm that the model proposed is flexible enough to cope with changes in operating conditions and has better diagnostic and generalizing capabilities.
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