Attention-guided graph isomorphism learning: A multi-task framework for fault diagnosis and remaining useful life prediction

Intelligent fault diagnosis and remaining useful life (RUL) prediction are essential for the reliable operation of rotating machinery. These technologies enhance safety, availability, and productivity in the manufacturing industry. Graph Convolutional Networks (GCNs), an extension of deep learning (DL) to graph data, provide superior performance due to their unique data representation capabilities. Traditional condition monitoring (CM) typically requires separate models for fault diagnosis and RUL prediction, leading to challenges such as ineffective knowledge sharing and high costs associated with preparing and deploying DL models. To address these issues, this study proposes a multi-task graph isomorphism network with an attention mechanism for simultaneous fault diagnosis and RUL prediction. This method considers the interrelationship between tasks, introducing both a parameter-sharing mechanism and a self-attention mechanism. Compared to traditional single-task methods, the proposed approach offers higher accuracy, greater practicality, and reduced costs of developing the model. The effectiveness of the method is validated using experimental degradation data, demonstrating its capability to address key issues in fault diagnosis and RUL prediction, exhibiting strong potential in practical applications.