Research on Mechanical Equipment Fault Diagnosis Method based on Transfer Learning

This paper presents a new approach for transfer learning in the mechanical fault diagnosis area, solving the problem of having too few labelled data in target domains with differing operating environments. The method combines sophisticated domain adaptation strategies with deep learning models for efficient knowledge transfer between source and target domains. As demonstrated by extensive experiments on bearing fault datasets, the novel approach delivers impressive accuracy in the target domains with sparse labelled data, achieving 94.67% which is a 16.03% increment compared to standard methods. The method is based on adaptive feature extraction, effective distribution alignment, and other domain adaptation techniques, which all together achieve high computational efficiency, demonstrated by an average inference time of 16.4ms. The numerous studies carried out ensure the effectiveness of the method under various working conditions and different faults, proving its industrial relevance. The work provides new directions for intelligent fault diagnosis through the development of a comprehensive approach for knowledge transfer in mechanical systems across domains.