An enhanced filtering-dynamic matching-coordinated interaction framework for open-set fault diagnosis of rolling bearings

Abstract Industrial fault diagnosis often requires open-set domain adaptation, 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 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%, unknown-class accuracy 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.