A bearing fault diagnosis method for unknown operating conditions based on differentiated feature extraction

Under unknown operating conditions, the domain generalization approach based on domain metrics is commonly used for rolling bearing fault diagnostics. Nevertheless, in the event of equipment failure under unknown operating conditions, focusing solely on the transferable characteristics across domains may result in the unintentional neglect of domain-specific features. To address the problems mentioned, the present study introduces a feature decomposition learning method that simultaneously extracts inter-domain transferable and domain-specific features. This method aims to obtain richer feature information by constructing different feature extractors. For the extraction of transferable features, a joint metric method based on central moment differences is devised. A difference maximization method is employed to extract domain-specific features. The experimental findings demonstrate that the proposed technique exhibits greater defect detection capacity across two datasets.