A Multigranularity Fuzzy Inference Approach for Out-of-Distribution Detection in Fault Diagnosis

The intelligent fault diagnosis has achieved notable success in identifying known mechanical failures; however, reliably detecting out-of-distribution (OOD) faults remains a key challenge to achieve the diagnostic robustness. In industrial applications, vibration signals are typically collected as time-series data whose dynamic characteristics vary with load, speed, and environmental interference, with weak early fault patterns that blur class boundaries. As a result, models trained under limited laboratory conditions inevitably encounter unseen OOD inputs after deployment, requiring the ability to recognize and reject them reliably. Existing representation- and similarity-based OOD methods have shown promise but typically rely on single-granularity prototypes, capturing only coarse similarity structures and overlooking latent subclass relations—thus limiting the generalization under complex degradation modes. To address these limitations, we propose a multigranularity fuzzy inference (MgFI) framework for enhanced uncertainty quantification in fault diagnosis. MgFI models fine-grained subclass memberships on a hyperspherical manifold, aggregates them into class-level fuzzy sets, and infers coarse-grained In-distribution (ID) confidence through the hierarchical fuzzy reasoning. Extensive experiments demonstrate that MgFI substantially improves the OOD detection accuracy and provides a principled, interpretable framework for trustworthy open-set industrial diagnostics.