A cross-condition fault diagnosis method for rolling bearings based on multi-source domain adversarial feature fusion

Currently, rolling bearing fault diagnosis faces dual challenges: significant variations in sample feature distributions caused by changes in operating conditions, and the scarcity of labeled fault samples. These factors severely undermine the generalization capability of traditional data-driven methods. To address these challenges, a multi-source domain feature fusion-domain adversarial neural network (MSDF-DANN) fault diagnosis model is proposed. Unlike conventional approaches that rely on unstable time-frequency representations, this study first employs variational mode decomposition and symmetric dot pattern to transform one-dimensional signals into two-dimensional snowflake maps, providing a geometrically consistent feature representation resilient to operating condition variations. Subsequently, a novel three-stage convolutional fusion module is designed to perform deep nonlinear integration of heterogeneous features from multiple source domains before adversarial adaptation. Furthermore, the mechanism of the adversarial weight is theoretically analyzed and experimentally verified to balance domain alignment and class discriminability. Finally, experiments on the Case Western Reserve University and Dynamic Diagnosis System datasets show that even with limited samples, MSDF-DANN has higher diagnostic accuracy and stability.