Uncertainty‐aware evidential prototype network with few‐shot class‐incremental learning for open‐set fault diagnosis

Abstract

Fault diagnosis plays an important role in process monitoring under closed‐set settings. However, the unknown faults may be misdiagnosed as one of the known classes with overconfidence, leading to unreliable decisions and process unsafety. Besides, to distinguish multiple unknown fault modes, existing methods need to be completely retrained with sufficient measurements under faulty conditions. However, model retraining is time‐consuming and the labelled faulty data is rare in real industrial processes. To solve the above problems, a novel uncertainty‐aware evidential prototype network (UEPN) with few‐shot class‐incremental learning (FSCIL) for open‐set fault diagnosis (OSFD) is proposed. First, the prototype network (PN) based feature extractor is proposed to learn discriminative embeddings for known classes and preserve more embedding space for unknown ones. An uncertainty‐aware evidential classifier is further developed with uncertainty quantification to identify unknown faults and avoid overconfident misclassification. Then, to continuously identify multiple unknown fault modes, the FSCIL procedure is designed, which enables UEPN to be incrementally updated with few‐shot data of an unknown fault class. Finally, the proposed method is evaluated on the Tennessee Eastman process (TEP) and the vinyl acetate monomer (VAM) plant model. The proposed method achieves up to 10.8% higher fault diagnosis rate (FDR), 5.4% lower false positive rate (FPR), and 8.5% higher true positive rate (TPR) than the comparison methods for OSFD task of continuously distinguishing multiple unknown fault modes.