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

Weiguo Li, Naiyuan Fan, Xiang Peng, Changhong Zhang, Mingyang Li, Xu Yang and Lijuan Ma ()
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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

Energies, 2024, vol. 17, issue 19, 1-13

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.

Keywords: motor bearings; fault diagnosis; feature extraction; signal reconstruction; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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