Multiclassifier-Weighted Adversarial Network-Based Open-Set Fault Diagnosis for Multimode Chemical Processes

Owing to the complexity and uncertainty of industrial production, unknown faults inevitably occur. However, most existing methods struggle to effectively identify unknown fault categories in a new operating mode, and often either treat known and unknown categories equally or exhibit a bias toward known classes. To tackle these issues, this paper proposes a Multi-Classifier-Weighted Adversarial Network for Open-set fault diagnosis in industrial processes. During the adversarial training between the feature extractor and the extended classifier, ambiguous samples exhibit a higher probability of being biased toward the unknown category. Furthermore, an auxiliary classifier is designed to compute the membership of samples to the unknown class, thereby enabling adaptive and dynamic adjustment of the bias intensity toward the unknown category during adversarial training. Experiments on two chemical fault datasets verify that the proposed model can simultaneously identify known and unknown faults, outperforming other diagnostic models.