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Fault Diagnosis for Motor Bearings via an Intelligent Strategy Combined with Signal Reconstruction and Deep Learning

Author

Listed:
  • Weiguo Li

    (Ultra High Voltage Transmission Company Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510000, China)

  • Naiyuan Fan

    (Henan Pinggao Electric Co., Ltd., Pingdingshan 476000, China)

  • Xiang Peng

    (Ultra High Voltage Transmission Company Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510000, China)

  • Changhong Zhang

    (Ultra High Voltage Transmission Company Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510000, China)

  • Mingyang Li

    (Ultra High Voltage Transmission Company Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510000, China)

  • Xu Yang

    (Henan Pinggao Electric Co., Ltd., Pingdingshan 476000, China)

  • Lijuan Ma

    (Henan Pinggao Electric Co., Ltd., Pingdingshan 476000, China)

Abstract

To overcome the incomplete decomposition of vibration signals in traditional motor-bearing fault diagnosis algorithms and improve the ability to characterize fault characteristics and anti-interference, a diagnostic strategy combining dual signal reconstruction and deep learning architecture is proposed. In this study, an improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD)-based signal reconstruction method is first introduced to extract features representing motor bearing faults. A feature matrix construction method based on improved information entropy is then proposed to quantify these fault features. Finally, a fault diagnosis algorithm architecture integrating a multi-scale convolutional neural network (MSCNN) with attention mechanisms and a bidirectional long short-term memory network (BiLSTM) is developed. The experimental results for four fault states show that this model can effectively extract fault features from original vibration signals and, compared to other fault diagnosis models, offer high diagnostic accuracy and strong generalization, maintaining high accuracy even under varying speeds and noise interference.

Suggested Citation

  • Weiguo Li & Naiyuan Fan & Xiang Peng & Changhong Zhang & Mingyang Li & Xu Yang & Lijuan Ma, 2024. "Fault Diagnosis for Motor Bearings via an Intelligent Strategy Combined with Signal Reconstruction and Deep Learning," Energies, MDPI, vol. 17(19), pages 1-13, September.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4773-:d:1484637
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