Small sample fault diagnosis based on Laplace wavelet convolution and BiGRU

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (24) : 41-50.

LUO Hao1，HE Chao1，CHEN Biao1，LU Yanping1，ZHANG Xin2，ZHANG Li1

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Abstract

Targeting the problems that rolling bearings usually work under complex conditions, causing breakdown easily and small training samples. A fault diagnosis method with global average pooling (GAP) and fusion of dual Laplace wavelet convolution and Bidirectional Gated Recurrent Unit(DLWCB) is proposed. Firstly, Laplace wavelet convolution is utilized to transform original signals from time to frequency domain, and then the multi-scale and spatiotemporal characteristics of small samples are mined by dual convolution and BiGRU. In addition, GAP is designed to reduce the amount of parameters of the model and integrate the spatiotemporal characteristics of GRUs. From the optimization algorithms and objective functions, label smoothing and AdamP are introduced to improve the capacity of DLWCB to cope with small samples, and finally achieve fault diagnosis under complex conditions. In two rolling bearing data sets, training can be completed in 50 seconds under limited noise samples, and the accuracy is over 98%. The proposed method has the better capacities of generalization, robustness and diagnosis efficiency.
Key words: Laplace wavelet convolution kernel; bidirectional gated recurrent unit; label smoothing; fault diagnosis; small sample

Key words

Laplace wavelet convolution kernel / bidirectional gated recurrent unit / label smoothing / fault diagnosis / small sample

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LUO Hao1，HE Chao1，CHEN Biao1，LU Yanping1，ZHANG Xin2，ZHANG Li1. Small sample fault diagnosis based on Laplace wavelet convolution and BiGRU[J]. Journal of Vibration and Shock, 2022, 41(24): 41-50

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