An Online Learning Framework for Fault Diagnosis of Rolling Bearings Under Distribution Shifts

Fault diagnosis of rolling bearings is crucial for ensuring the maintenance and reliability of industrial equipment. Existing cross-domain diagnostic methods often struggle to maintain performance under evolving mechanical and environmental conditions. This limits their robustness in long-term real-world deployments. To address this, we propose a novel online learning framework that continuously adapts to distribution shifts using streaming vibration data. Specifically, the proposed framework consists of three core modules: the Feature Extraction Module that encodes raw vibration signals into low-dimensional latent representations; the Fault Sample Generation Module (comprising a generator and discriminator network) that synthesizes diverse fault samples conditioned on normal-condition data; and the Classification Module that incrementally adapts by leveraging both synthesized fault samples and streaming normal-condition signals. We also introduce a domain-shift indicator ScoreODS to dynamically control the transition between prediction and fine-tuning phases during deployment. Extensive experiments on both public and private datasets demonstrate that the proposed method outperforms the most competitive method, achieving about a 4% improvement in diagnostic accuracy and enhanced robustness for long-term fault diagnosis under distribution shifts.