A FD-Transformer method for fault diagnosis of mechanical equipment

ZHAO Zhihong1,2, LI Chunxiu2, LI Lehao2, YANG Shaopu1

Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (8) : 326-333.

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PDF(2094 KB)
Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (8) : 326-333.

A FD-Transformer method for fault diagnosis of mechanical equipment

  • ZHAO Zhihong1,2, LI Chunxiu2, LI Lehao2, YANG Shaopu1
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Abstract

With the development of mechanical equipment fault diagnosis technology, people pay more and more attention to using deep learning technology to judge the type of equipment fault. At present, Transformer model based on attention mechanism has better feature extraction ability than Convolutional Neural Network, and has been successfully applied in the fields of natural language processing and computer vision. A Transformer method for mechanical equipment fault diagnosis (FD-Transformer) is proposed. Firstly, the original vibration signal is enhanced by dropout technology to improve the generalization ability of the model; Then the matrix form is obtained by multi-channel one-dimensional convolution; Then, the Encoder structure connected by Dense is used to extract the fault features of mechanical equipment. Finally, the fault diagnosis results are obtained by using the classification module. The experimental results show that the model achieves a fault recognition rate of more than 99% on both data sets. Compared with CNN, it can better extract the fault characteristics of mechanical equipment, and has certain engineering application value.

Key words

Transformer / Attention mechanism / fault diagnosis / deep learning

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ZHAO Zhihong1,2, LI Chunxiu2, LI Lehao2, YANG Shaopu1. A FD-Transformer method for fault diagnosis of mechanical equipment[J]. Journal of Vibration and Shock, 2023, 42(8): 326-333

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