Zero-Shot Fault Diagnosis in Manufacturing Processes via Attribute Co-Occurrence Relationships

In the transition from pilot to mass production, certain fault classes lack available samples, creating a zero-shot condition that challenges the training of fault diagnosis models. Zero-shot learning (ZSL) maps features to shared attribute vectors formed by engineering knowledge information, offering a potential solution. In manufacturing processes, disturbances propagate across operations, causing multiple attributes to co-occur. However, the internal co-occurrence relationships among attributes are often overlooked, which may lead to discrepancies in attribute prediction. To address this issue, we propose a zero shot fault diagnosis method for manufacturing processes that leverages attribute co-occurrence relationships to identify unseen faults. To capture this relationship, mutual information is first employed to quantify attribute co-occurrence and construct an adjacency matrix of attribute relations. In parallel, distributional features are extracted using multi-delay ordinal patterns, and a distance-based loss function is designed to align these features with the attributes. The adjacency matrix and distributional features are then input into a graph convolutional network, with attribute relationships embedded into the distributional features to maintain consistency. Finally, distributional features support zero-shot fault diagnosis via mapping and similarity measures. Experimental validation on the Tennessee Eastman Process (TEP) and fuel rods manufacturing process demonstrates the effectiveness of the proposed method.