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Fault diagnosis method of wind turbine rolling bearing based on S-CBiGRU

Published:2023-04-20 author:SHI Zong-hui, CHEN Chang-zheng, TIAN Miao,et al. Browse: 432 Check PDF documents

Fault diagnosis method of wind turbine rolling bearing based on S-CBiGRU

SHI Zong-hui1, CHEN Chang-zheng1,2, TIAN Miao1, AN Wen-jie1, SUN Xian-ming3

(1.School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China;

2.Liaoning Vibration and Noise Control Engineering Research Center, Shenyang 110870, China;

3.Ningbo Kunbo Measurement and Control Technology Co., Ltd, Ningbo 315200, China)

Abstract: For wind turbine rolling bearing vibration signal, there were problems with non-linear, non-smooth characteristics, which led to features that were not easy to be extracted. The S-CBiGRU fault diagnosis method based on S-transform, convolutional neural network(CNN), and bidirectional gated recurrent unit(BiGRU)was proposed. Firstly, in order to improve the accuracy of wind turbine rolling bearing fault diagnosis, the S-transform was used to carry out multi-resolution time-frequency analysis on the vibration signals collected from wind farms. The one-dimensional vibration signals were transformed into two-dimensional time-frequency images containing both temporal and spatial feature information. Secondly, the time-frequency maps obtained from the S-transform were input into the CBiGRU network model, and the spatial features of the vibration signals were extracted by the CNN convolutional pooling layer. Followed by the BiGRU structure, the time series features of the vibration signal were extracted. Finally, in order to verify the effectiveness of the above bearing fault diagnosis method, the experimental data of the wind turbine bearing was collected and input into the model for diagnosis experiments. The diagnostic results show that the accuracy of the S-CBiGRU method in wind turbine bearing fault diagnosis reaches 93.17%, the classification effect is better than other deep learning algorithms. The results show that the S-CBiGRU fault diagnosis method is feasible and provides a new way to diagnose rolling bearing faults in wind turbines.

Key words: timefrequency analysis; spatial features; time series features;S transform; convolutional neural network(CNN); bidirectional gated recurrent unit(BiGRU)

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