Research on Fault Diagnosis Method of Rotary Machinery Based on Improved Transformer


  • Haijie Zhi
  • Jinkui Wang
  • Haitao Zhang
  • Yongkang Hou
  • Qishun Yang



Rotating machinery, fault diagnosis method, Transformer, attention mechanism.


In modern industry, rotating machinery plays a crucial role. These rotating machines are not only fundamental components of power generation and propulsion systems but also key factors for their efficient operation. Harsh operating environments often lead to failures in critical components like gears and bearings in rotating machinery, which can directly result in equipment malfunction. Therefore, authentic fault diagnosis of these primary building blocks in rotating machinery is of pivotal value for improving its reliability and safety during operation. This research proposes a fault diagnosis strategy for rotating machinery rooted on time series Transformer and validates the effectiveness of the proposed approach. Firstly, a time series Transformer model is designed, which incorporates modules like time series embedding, attention mechanism, and multi-layer perceptron to promptly approach 1D oscillatory motion signal data. Subsequently, the hypothetical model is trained and tested on multiple public datasets, and its fault diagnosis results are compared with existing achievements in the literature. The proficiency of the model is thoroughly verified, and the fault diagnosis performance of this proposed approach surpasses many existing fault diagnosis methods.


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How to Cite

Zhi, H., Wang, J., Zhang, H., Hou, Y., & Yang, Q. (2023). Research on Fault Diagnosis Method of Rotary Machinery Based on Improved Transformer. Academic Journal of Science and Technology, 7(1), 79–85.