Small sample bearing fault diagnosis based on compressed sensing reconstruction and dictionary transfer

SUN Jiedi1,2,ZHAO Binji1,WEN Jiangtao3,SHI Peiming3

Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (5) : 62-71.

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PDF(3265 KB)
Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (5) : 62-71.

Small sample bearing fault diagnosis based on compressed sensing reconstruction and dictionary transfer

  • SUN Jiedi1,2,ZHAO Binji1,WEN Jiangtao3,SHI Peiming3
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Abstract

To address the problem of the severe shortage of label samples for intelligent bearing fault diagnosis in practical applications, this paper proposes a data enhancement method combining compressed sensing, dictionary learning and transfer for small sample fault diagnosis research. Firstly, the source domain label data is used to generate a specific source domain dictionary through wavelet packet dictionary learning and optimization processing, and the shared representation coefficients are obtained to acquire the intrinsic fault information; then a small amount of target domain signals are used to fine-tune the shared representation coefficients, and the source domain dictionary is updated to generate a transfer dictionary; finally, a large number of new samples with target domain characteristics are generated through the representation coefficients and transfer dictionary to achieve data enhancement. The data augmentation algorithm is validated using a commonly used deep fault diagnosis network, and the results show that the signals generated by the method have valid information about the fault and can be used for model training and identification with good diagnostic performance. The proposed method provides a new idea for the small sample fault diagnosis.

Key words

Fault diagnosis / Data augmentation / Compressed Sensing reconstruction / Transferred dictionary

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SUN Jiedi1,2,ZHAO Binji1,WEN Jiangtao3,SHI Peiming3. Small sample bearing fault diagnosis based on compressed sensing reconstruction and dictionary transfer[J]. Journal of Vibration and Shock, 2024, 43(5): 62-71

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