An Enhanced Filtering-Dynamic Matching-Coordinated Interaction Framework for Open-set Fault Diagnosis of Rolling Bearings

Industrial fault diagnosis often requires open-set domain adaptation (ODA), in which target data may contain fault types absent from the source. An Enhanced Filtering-Dynamic Matching-Coordinated Interaction (EFDMCI) framework is introduced to address three practical obstacles: (i) reliance on manually tuned thresholds for unknown detection; (ii) negative transfer caused by indiscriminate alignment; and (iii) weak coupling among modules. Within EFDMCI, Enhanced Filtering (EF) is used to perform threshold-free, rank-based sample selection, and a binary gate is trained to route unknown samples. Dynamic Matching (DM) is used to align marginal and conditional distributions with reliability-aware, dynamically updated weights. Optimization is stabilized by a Coordinated Interaction (CI) scheme that alternates EF and DM updates. Across intra- and cross-machine transfers, mean accuracies of 97.45% and 95.02% were obtained, respectively, together with strong open-set robustness (OS* 97.44%, OS 90.36%, UNK 71.55%, H-score 82.48%), as detailed in the Results. These findings indicate that reliable recognition of known and unknown faults can be achieved without manual thresholds, enabling online monitoring and cross-facility deployment.