Open-set fault diagnosis based on dynamic triple multivariate guided structural constraints

Abstract Existing deep learning-based models for mechanical fault diagnosis perform well in identifying predefined faults, but these models substantially degrade in performance when they encounter unknown faults. Thus, it is crucial to investigate open-set fault diagnosis that can handle unknown faults more efficiently. Current methods for open-set fault diagnosis in machinery face challenges by the lack of hierarchical structure in feature representation and the overlapping regions of known and unknown sample distributions. To solve these problems, we propose a composite dual-branching dynamic triplet multivariate constrained (CDDTMC) model for mechanical open-set fault diagnosis. The CDDTMC framework consists of three main core modules: a feature extraction module, a structural constraint module and a fault diagnosis module. In the feature extraction module a composite two-branch network is designed to extract hierarchical feature representations from known samples. After extracting the sample features, it represents the samples with structural constraints using multivariate constraints based on bidirectional dynamic triplet loss to achieve discriminativeness and compactness. Determining the optimal decision boundary for each category based on the structural constraints and uses a distance-based diagnostic algorithm to identify fault diagnosis. We conducted experiments on two publicly available bearing datasets to validate the performance of the model. The results show that the model improves the Average Accuracy Classification (ACC) by 10.73% and 13.84%, respectively, compared to other comparative model.