Zero-Shot Fault Diagnosis in Industrial Processes Using Graph-Regularized Coupled Dictionary Learning

In real-world industrial processes, it is crucial to avoid damaging faults to ensure steady operations. However, new faulty conditions can arise in variable production environments. Traditional fault diagnosis methods perform poorly with few or no fault data for training. To address this issue, we propose a graph-regularized coupled dictionary learning (GRCDL) method for zero-shot fault diagnosis in industrial processes. Our method enhances the coupled dictionary learning (CDL) framework by integrating class prototype learning, which utilizes the rich information of seen class faults in the data feature space. A feature correlation function is also designed to facilitate the transformation and reconstruction of data and semantic features in the shared space, preventing certain classes from becoming the nearest neighbors of unrelated classes in a low-dimensional semantic space. To mitigate noise interference and preserve local manifold structures, the graph-regularized constraint is developed to enhance the robustness and locality of the coupled dictionaries. Considering the different underlying distributions between unseen and seen faults, a novel domain adaptation function is proposed. This function uses the learned coupled dictionaries as mediums for knowledge transfer and update from seen to unseen classes and effectively learns the class prototypes of unseen faults. Extensive experiments in industrial processes verify the superior performance of our method for zero-shot fault diagnosis.