Prior-Knowledge-Guided Graph Attention Network for Fault Diagnosis of Engine Valve Clearance

Fault diagnosis of diesel engines is a critical task in the operation and maintenance of complex equipment. Diesel engine fault diagnosis technology based on deep learning has seen widespread development due to its powerful feature learning and fault classification capabilities. However, traditional data-driven deep learning models cannot explicitly uncover relationships between signals, which hinders better fault information capture. Therefore, this paper proposes a diesel-engine valve-clearance fault diagnosis method driven by a combination of knowledge and data. Firstly, the original signals are converted into graph data with a topological structure based on the spatiotemporal relationships of events occurring within the cylinder, thereby uncovering the intrinsic structural information of the samples. Then, the graph structure is input into a graph convolutional attention network to extract features and learn fault patterns. Valve fault experiments were conducted on a diesel engine test bench, and the results indicate that the proposed knowledge and data-driven deep learning fault diagnosis model achieves better diagnostic performance and clearer interpretability compared to traditional data-driven deep learning fault diagnosis models, and it still has a relatively high accuracy in a diagnostic environment with scarce data.