An Adversarial Robustness Enhancement Framework for Industrial Fault Diagnosis System With Spectral and Data Imbalance Awareness

Cyber-physical systems (CPSs), which integrate sensing, computation, and control, are widely deployed in safety-critical applications such as industrial monitoring and predictive maintenance. Industrial fault diagnosis systems (IFDSs), as a representative CPS, enable intelligent health monitoring but are increasingly vulnerable to stealthy adversarial attacks—carefully crafted input perturbations that mislead models while remaining imperceptible. These attacks compromise system safety, highlighting the urgent need for learning-based security control in CPS. This article proposes multilayered noisy mixup (MLNM)-frequency shift (FrSh), a theoretically grounded adversarial defense framework for IFDS under real-world conditions involving nonstationary signals and class imbalance. The framework integrates two components: multilayered noisy mixup (MLNM) and frequency shift (FrSh). MLNM injects structured additive and multiplicative noise during training and is theoretically proven to introduce first- and second-order regularization terms in the loss function, enhancing generalization and gradient stability. FrSh performs spectrum-aware transformations that preserve conjugate symmetry and spectral integrity, with formal guarantees of aliasing-free, reversible frequency mapping, facilitating effective learning of high-frequency diagnostic features. Extensive experiments on benchmark datasets demonstrate that MLNM-FrSh significantly improves adversarial robustness and standard accuracy compared to state-of-the-art baselines. This work contributes a reliable and theoretically principled solution for attack-resilient fault diagnosis, advancing the broader goal of intelligent security control in CPS.