Fault dynamics-assisted open-set domain adaptation for fault diagnosis of roller bearings

Abstract Data-driven domain adaptation methods have attracted significant attention in fault diagnosis. However, industrial deployment remains challenging due to scarce labeled data and diverse operating conditions. To tackle these challenges, a fault dynamics-assisted open-set domain adaptation (FD-ODA) was proposed for fault diagnosis across digital and physical domains. A fault dynamics model is built by the augmented Lagrange multi-body dynamics approach to simulate typical bearing faults, including raceway defects and cage pillar fractures, and generate dataset of labeled vibration signals. The simulated signals are validated against experimental data obtained from test bench measurements. And to improve diagnostic performance, an ODA model is designed to mitigate distribution shifts between virtual and real domains. Experiments show that FD-ODA achieves superior performance in cross-domain open-set bearing fault diagnosis.