Qinghua Jiang and Xiangdong Bo
Mechanical fault diagnosis, deep learning, artificial intelligence, robustness
With the development of modern mechanical engineering and data science, mechanical fault diagnosis has become a crucial research field. Especially in ensuring the continuity of production lines, improving system efficiency and ensuring personnel safety, its value is immeasurable. This study focuses on the application of deep learning and data analysis in mechanical fault diagnosis, and explores their robustness and interpretability in practice. Through the comprehensive evaluation of several algorithms and methods, a new fault diagnosis method combining recurrent neural network (RNN) and interpretive artificial intelligence (XAI) is proposed. The results show that the proposed method performs well in both accuracy and reliability. In general, this study not only provides a new perspective for mechanical fault diagnosis but also provides a valuable reference for practical application.
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