Toward regulation-friendly bearing fault diagnosis via structural embedding preserving machine unlearning

Bearing fault diagnosis supports predictive maintenance by detecting mechanical failures early and reducing downtime in industrial monitoring systems. Recent deep learning models have achieved strong performance in classifying fault types. In practical industrial applications, however, certain training data may need to be removed due to privacy regulations, contractual obligations, or data quality issues. Machine unlearning has therefore become increasingly relevant, although prior work has rarely studied it in bearing fault diagnosis. This paper provides a systematic analysis of existing unlearning methods for fault diagnosis systems and shows their poor performance on time-series vibration data typical of bearing faults. To overcome this limitation, a novel structural embedding preserving unlearning method, Soft-directed Embedding Unlearning (SEU) is proposed, which directly manipulates the embedding space by pushing forget samples away from original class clusters while preserving the structure of retain samples using soft examples near decision boundaries. We use a manifold similarity measure to quantify distance-based separation between forget and retain samples and show that SEU provides a stable approximation of the ideal manifold structure. Experiments on both public and private data show that SEU achieves more effective machine unlearning than the evaluated baselines.