A continual test-time domain adaptation method for online machinery fault diagnosis under dynamic operating conditions.

In practical industrial scenarios, monitoring data is collected in a streaming fashion under dynamic changes in operating conditions of mechanical systems, with continual covariate shift and label shift occurring in the collected data. Traditional transfer learning-based fault diagnosis methods typically involve pre-collecting substantial monitoring data for offline training and testing under static conditions. These approaches cannot adjust the model in real-time to continuous data shifts caused by dynamically changing conditions, resulting in a lack of adaptability and generalization. To overcome this practical challenge, a continual test-time domain adaptation (CTDA) approach with a teacher-student framework is developed for online machinery fault diagnosis under dynamic operating conditions in this study. Firstly, a class-balanced sampling mechanism is proposed to eliminate the impact of continual condition label shift by enforcing the model to learn from a uniform label distribution. Secondly, a joint positive-negative learning strategy is employed to guide model optimization and reduce the interference from pseudo-label noise. Lastly, the continual covariate shift is mitigated by performing the knowledge alignment between the teacher and student models. Comprehensive experiments on four rotating machinery datasets demonstrate that the proposed method improves average diagnosis accuracy by 3.78% in handling dynamic industrial streaming data compared to existing fault diagnosis methods.