Adversarial Retain-Free Unlearning for Bearing Prognostics and Health Management

Prognostics and health management (PHM) systems increasingly rely on machine learning for reliable bearing fault diagnosis. However, data deletion in PHM remains an open problem. Since retraining a model from scratch is often infeasible, this raises a new challenge. Moreover, in most cases, only the pretrained model and the user-requested data are available. Existing approaches under this constraint often fail to achieve proper unlearning and substantially degrade model performance, thereby disrupting the original embedding space and collapsing the structural integrity of the pretrained model. To address this, we propose adversarial retain-free unlearning (ARU). Our framework integrates adversarial samples generated from the pretrained model with a semantic-driven loss to preserve representational stability. Experiments on public and private bearing datasets demonstrate that ARU achieves unlearning efficacy and structural consistency comparable to Retrain while maintaining diagnostic accuracy. We believe that our proposed framework provides a practical and reliable solution for retain-free unlearning in real-world industrial PHM systems.