Volume 34 Issue 12
Dec.  2019
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ZHANG Xiangyang, CHEN Guo, HAO Tengfei. Convolutional neural network diagnosis method of rolling bearing fault based on casing signal[J]. Journal of Aerospace Power, 2019, 34(12): 2729-2737. doi: 10.13224/j.cnki.jasp.2019.12.022
Citation: ZHANG Xiangyang, CHEN Guo, HAO Tengfei. Convolutional neural network diagnosis method of rolling bearing fault based on casing signal[J]. Journal of Aerospace Power, 2019, 34(12): 2729-2737. doi: 10.13224/j.cnki.jasp.2019.12.022

Convolutional neural network diagnosis method of rolling bearing fault based on casing signal

doi: 10.13224/j.cnki.jasp.2019.12.022
  • Received Date: 2019-05-31
  • Publish Date: 2019-12-28
  • A fault diagnosis method based on convolutional neural network (CNN) was proposed for the weak fault of the engine casing under the rolling bearing fault excitation. The one-dimensional original signal was converted into image signal by using three preprocessing methods: matrix graph method, kurtosis graph method and wavelet scale spectrum. Then the convolutional neural network was used to identify the fault. Through comparative analysis, the fault identification rate of rolling bearing was 95.82%, which was higher than other vibration signal pretreatment methods. At the same time, the fault recognition rate of convolutional neural network was about 7% higher than that of traditional support vector machine (SVM) because it can use deep network structure to extract the fault characteristics of rolling bearing adaptively. The results show that the proposed method is feasible and effective, and has a good generalization ability and robustness.

     

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