Generalized Class-Incremental Lifelong Transfer Diagnosis of Machinery Faults in Industrial Streaming Data and Time-Varying Working Conditions

Internet of Things technology has greatly advanced the development and application of data-driven fault diagnostics and prognostics for industrial equipment. Industrial streaming data processing remains a challenge for intelligent fault diagnosis to continuously learning fault knowledge while retaining strong anti-forgetting ability. Recently, class-incremental learning has gained attention in data-driven fault diagnosis, since it enables to sequentially integrate new fault modes from industrial streaming data while maintaining previously learned knowledge. However, incremental transfer diagnosis in industrial streaming data and time-varying working conditions remain largely unexplored, and challenges such as the stability–plasticity dilemma and limited replay techniques still constrain diagnostic performance. To tackle these issues, we propose a generalized class-incremental lifelong transfer diagnosis (GCILTD) framework. First, a dynamic network expansion strategy is developed to overcome the stability-plasticity dilemma effectively, enabling the incremental model to capture new fault information while preserving prior knowledge. Then, a multi-stage training strategy is proposed to enhance the generalization of the dynamic network, further boosting both knowledge retention and adaptability. Furthermore, a dual-level memory buffer is first designed to enhance the class-incremental transfer diagnosis under time-varying working conditions. Finally, the proposed GCILTD framework is verified on two mechanical fault datasets. Experiment results demonstrate that our proposed GCILTD framework achieves advantageous diagnostic accuracies of 93.88% and 88.51% along with the lowest forgetting rates of 2.41% and 6.09% in different class-incremental transfer scenarios under industrial streaming data and time-varying working conditions, outperforming cutting-edge class-incremental learning approaches.