Graph neural network-based bearing fault diagnosis with adaptive activation functions and interpretability enhancement

Abstract Bearing fault diagnosis poses challenges such as limited feature extraction, fixed activation functions, low interpretability, and unstable diagnostic performance in graph neural networks. This necessitates the development of adaptive learning and optimization strategies. This paper introduces simulated annealing (SA)-Kolmogorov–Arnold Network (KAN)-GraphSAGE, a novel graph neural network framework for feature extraction in unsupervised intelligent bearing failure diagnosis. The framework leverages the KAN with SA optimization to enhance its capabilities. It features a specially designed KAN layer that replaces traditional multilayer perceptrons, enabling adaptive nonlinear activation functions to effectively capture complex fault feature patterns. Moreover, a KAN aggregator, utilizing a message-passing mechanism, integrates neighborhood structural information with learnable transformations to enhance feature representation. Hierarchical optimization is achieved through a combination of the SA algorithm and parameter sensitivity analysis to ensure model stability. The dual interpretability framework combines the interpretability of KAN networks with knowledge graphs to enhance semantic understanding during feature learning. An unsupervised training mechanism employing contrastive learning and feature reconstruction facilitates efficient feature extraction without extensive labeled data. Validation on Case Western Reserve University and Machinery Failure Prevention Technology bearing datasets demonstrates that SA-KAN-GraphSAGE surpasses seven mainstream approaches and two state-of-the-art methods in clustering quality, representation capability, and numerical stability metrics. This study establishes a robust technical foundation for equipment health monitoring in intelligent manufacturing.