Weighted Conditional Discriminant Analysis for Unseen Operating Modes Fault Diagnosis in Chemical Processes

One challenge faced by data-driven fault diagnosis methods is that they may perform well over the operating modes where the historical data are collected, but fail to generalize to unseen modes that have never appeared before. That is one of the root causes that have prevented many advanced fault diagnosis methods from being widely accepted by the chemical industry. Consequently, it is significant to develop a novel fault diagnosis method, which can build a model to determine the type of faults occurred on unseen modes. On the other hand, one chemical process generally experiences multiple operating modes, from which common knowledge of these modes can be extracted and be applied to an unseen mode. To this end, a novel weighted conditional discriminant analysis (WCDA) algorithm is proposed by adopting the context of domain generalization (DG) approaches to leverage and distill the knowledge from historical modes for unseen modes of fault diagnosis. Specifically, a novel variable weighting scheme is developed based on the Kullback–Leibler divergence between features of different modes. Then, a fault diagnosis model is constructed by learning a classifier and invariant feature representation simultaneously. Moreover, WCDA is extended to the context of domain adaptation (DA), where the performance of the fault diagnosis model is further improved by leveraging the unlabeled data collected from a new mode. Empirical results on a numerical example, the Tennessee Eastman process, and continuous stirred tank chemical reactor demonstrate the effectiveness of our method.