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| A FD-Transformer method for fault diagnosis of mechanical equipment |
| ZHAO Zhihong1,2, LI Chunxiu2, LI Lehao2, YANG Shaopu1 |
1.State Key Lab of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;
2.School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China |
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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.
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Received: 24 February 2022
Published: 28 April 2023
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