Fault Diagnosis Method for Bearing Based on Digital Twin

The bearing is an essential component of rotating machinery, as its reliability and running state have a direct impact on the machinery’s performance. Considering that deep learning-based fault diagnosis methods for bearing require a large amount of labelled sample data, a novel fault diagnosis fram...

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Bibliographic Details
Published in:Mathematical problems in engineering 2022-11, Vol.2022, p.1-15
Main Authors: Xie, Xuyang, Yang, Zichun, Wu, Wenhao, Zhang, Lei, Wang, Xuefeng, Zeng, Guoqing, Chen, Guobing
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Bearings
Component reliability
Deep learning
Digital twins
Engineering
Fault diagnosis
Machinery
Methods
Neural networks
Performance degradation
Rotating machinery
Self organizing maps
Sensors
Signal processing
Simulation
Support vector machines
Wavelet transforms
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Summary:The bearing is an essential component of rotating machinery, as its reliability and running state have a direct impact on the machinery’s performance. Considering that deep learning-based fault diagnosis methods for bearing require a large amount of labelled sample data, a novel fault diagnosis framework based on digital twin is proposed. In the case of fault data available, self-organizing maps with minimum quantization error and support vector machine are employed to analyze the data. Where fault data is unavailable, a bearing digital twin model is first constructed to simulate the data, and the convolutional neural network combined with transfer learning is utilized to diagnose the bearing faults. Then, the law of bearing performance degradation is investigated. The effectiveness of the proposed method is verified using bearing vibration data.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/2982746