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  • ISSN: 0577-6686

Journal of Mechanical Engineering ›› 2023, Vol. 59 ›› Issue (20): 215-224.doi: 10.3901/JME.2023.20.215

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Research Progress and Challenges of Interpretable Mechanical Intelligent Diagnosis

LIN Jing, JIAO Jinyang   

  1. School of Reliability and Systems Engineering, Beihang University, Beijing 100191
  • Received:2023-03-23 Revised:2023-09-16 Online:2023-10-20 Published:2023-12-08

Abstract: The intelligent diagnosis method of machinery based on deep neural networks has developed rapidly in recent years, and various model methods have emerged one after another. However, the performance of such methods is mainly in the laboratory environment, and there are few applications in actual industrial scenarios. The main reason is that the nonlinear transformation inside the model is quite complex, and the feature extraction mechanism is difficult to understand, leading to the user's untrust in decision-making. Especially for some key equipment, if the reason why the model gets the diagnosis conclusion cannot be known in advance, taking measures rashly will hide considerable risks. In light of this, more and more attention has been paid to the interpretability of intelligent fault diagnosis recently, and some scholars have reached some preliminary conclusions. In order to deepen research and promote the development of the field, various paradigms of interpretability of deep neural networks are categorized and discussed, then, current developments in interpretable intelligent fault diagnosis of machinery are detailed, and finally, existing challenges and future research directions are discussed and summarized.

Key words: deep neural networks, interpretability, mechanical equipment, intelligent fault diagnosis

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