A novel few-shot cross-domain fault diagnosis method enhanced by feature fusion and ReLU-KAN

Abstract The increasing complexity of industrial machinery necessitates reliable fault diagnosis systems to prevent safety risks and operational disruptions. Rolling bearings, as critical components, require precise fault detection, yet existing deep learning models struggle with generalization under data scarcity and cross-domain distribution shifts. To address this, a novel few-shot cross-domain fault diagnosis method enhanced by feature fusion and ReLU-KAN (FFRK), is proposed. This method comprises a feature extraction and fusion module and a feature dimensionality reduction module. Within the prototypical network framework, EfficientViT is employed for feature extraction to capture the multi-scale characteristics of bearing fault signals, while Adaptive Average Pooling is utilized for feature fusion to comprehensively extract fault features. The ReLU-KAN module further enhances model robustness through feature dimensionality reduction. Extensive experiments on three bearing datasets demonstrate FFRK’s superior performance in cross-domain scenarios of speed variation and size variation, achieving state-of-the-art accuracy with minimal training samples. The contributions include: (1) A lightweight multi-scale feature extractor combining EfficientViT and Adaptive Average Pooling; (2) A ReLU-KAN module balancing nonlinear representation and computational efficiency; (3) Comprehensive validation in challenging industrial scenarios, confirming FFRK’s potential for real-time fault diagnosis.