Fault Diagnosis Method for Bearing Based on Digital Twin

• 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
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2022/2982746

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.