LAPSE:2021.0763
Published Article
LAPSE:2021.0763
Research on Rotating Machinery Fault Diagnosis Method Based on Energy Spectrum Matrix and Adaptive Convolutional Neural Network
Yiyang Liu, Yousheng Yang, Tieying Feng, Yi Sun, Xuejian Zhang
October 14, 2021
Traditional intelligent fault diagnosis methods focus on distinguishing different fault modes, but ignore the deterioration of fault severity. This paper proposes a new two-stage hierarchical convolutional neural network for fault diagnosis of rotating machinery bearings. The failure mode and failure severity are modeled as a hierarchical structure. First, the original vibration signal is transformed into an energy spectrum matrix containing fault-related information through wavelet packet decomposition. Secondly, in the model training method, an adaptive learning rate dynamic adjustment strategy is further proposed, which adaptively extracts robust features from the spectrum matrix for fault mode and severity diagnosis. To verify the effectiveness of the method, the bearing fault data was collected using a rotating machine test bench. On this basis, the diagnostic accuracy, convergence performance and robustness of the model under different signal-to-noise ratios and variable load environments are evaluated, and the feature learning ability of the method is verified by visual analysis. Experimental results show that this method has achieved satisfactory results in both fault pattern recognition and fault severity evaluation, and is superior to other machine learning and deep learning methods.
Keywords
convolutional neural network, dynamic adjustment of the learning rate, energy spectrum matrix, hierarchical fault diagnosis, rotating machinery
Suggested Citation
Liu Y, Yang Y, Feng T, Sun Y, Zhang X. Research on Rotating Machinery Fault Diagnosis Method Based on Energy Spectrum Matrix and Adaptive Convolutional Neural Network. (2021). LAPSE:2021.0763
Author Affiliations
Liu Y: Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy
Yang Y: Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; College of Information Science and Engineering, Northeastern University, Shenyang 110819, China [ORCID]
Feng T: Industrial Engineering Department, XIOLIFT, Hangzhou 311199, China
Sun Y: Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Information and Control Engineering Department, Shenyang Jianzhu University, Shenyang 110168, China
Zhang X: Industrial Engineering Department, XIOLIFT, Hangzhou 311199, China
Journal Name
Processes
Volume
9
Issue
1
First Page
pr9010069
Year
2020
Publication Date
2020-12-30
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr9010069, Publication Type: Journal Article
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LAPSE:2021.0763
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doi:10.3390/pr9010069
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Oct 14, 2021
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Oct 14, 2021
 
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Calvin Tsay
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