Learning Category-Invariant Disentangled Features for Domain Generalization in Machine Fault Diagnosis

Existing domain generalization (DG) methods typically align multidomain data into a shared feature space to extract domain-invariant representations for machine fault diagnosis. Departing from this alignment paradigm, this article proposes a novel learning category-invariant features (LCIFs) framework. Motivated by the concept of genetic markers, the LCIF explicitly decomposes machine health states into domain attributes (reflecting operating conditions) and category attributes (reflecting fault types), and disentangles category-invariant features from multidomain observations. To this end, a patch-level feature adaptive aggregation is built in the feature extraction module to suppress noise attributes, and…

• NN
  National Natural Science Foundation of China

  Award: 52405093
• NS
  National Science and Technology Major Project

  Award: J2019-IV-0001-0068
• NS
  Natural Science Foundation of Sichuan Province

  Award: 2025ZNSFSC1269