ASBR-CL: Stage-Aware Balanced Replay for Memory-Limited Continual Fault Diagnosis

Vibration-based sensing systems for deployed industrial fault diagnosis often face incremental fault classes, changing degradation stages, and limited permission to retain historical sensor streams. Static fault classifiers are therefore insufficient for online maintenance settings in which a model must learn new sensor-observed states while preserving previous diagnostic knowledge under a bounded memory budget. This paper proposes ASBR-CL, an adaptive stage-aware balanced replay framework for continual fault diagnosis under a fixed exemplar-memory budget. ASBR-CL combines dataset-adaptive exemplar memory, balanced replay between current data and retained exemplars, and a conditional validation checkpoint module that is enabled only when it improves balanced stage recognition. Experiments on SEU-enhanced92, the 92-dimensional feature construction for SEU, and XJTU-bearing23, the 23-dimensional feature construction for XJTU-SY, compare ASBR-CL with DGGN/MFF same-backbone continual-learning baselines and XJTU imbalance-aware variants under five random seeds and K=100 training exemplars. On SEU, the selected ASBR-CL setting reports 97.49±1.18 Average Accuracy, 90.37±4.53 Final Accuracy, 90.37±4.53 Macro Recall, and 11.50±5.25 Average Forgetting. On XJTU, the conservative ASBR-CL-BoundedVal-K100 setting is not a universal Final Accuracy winner: DGGN-ER reaches a slightly higher Final Accuracy (89.52±2.68 versus 89.05±2.48). The ASBR-CL evidence instead lies in balanced recognition and retention, with Macro Recall 75.00±2.00, Macro-F1 67.66±3.83, and Average Forgetting 20.39±4.89, compared with DGGN-ER at 63.94±7.50, 55.48±12.82, and 41.11±13.61. Additional validation-resource, RMS-derived stage-definition, and imbalance-aware baseline analyses show that the revised XJTU claim should be framed as more stable Macro Recall, Macro-F1, and forgetting control under memory-limited continual diagnosis, not as superiority on every accuracy metric.