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

被引:3
|
作者
Li, Weiguo [1 ]
Fan, Naiyuan [2 ]
Peng, Xiang [1 ]
Zhang, Changhong [1 ]
Li, Mingyang [1 ]
Yang, Xu [2 ]
Ma, Lijuan [2 ]
机构
[1] China Southern Power Grid Co Ltd, Ultra High Voltage Transmiss Co, Elect Power Res Inst, Guangzhou 510000, Peoples R China
[2] Henan Pinggao Elect Co Ltd, Pingdingshan 467000, Peoples R China
关键词
motor bearings; fault diagnosis; feature extraction; signal reconstruction; deep learning; EMPIRICAL MODE DECOMPOSITION;
D O I
10.3390/en17194773
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:13
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