Adversarial Learning From Imbalanced Data: A Robust Industrial Fault Classification Method

Data-driven models are revealed to be vulnerable to adversarial examples, so improving the model’s adversarial robustness has attracted extensive research. However, in real-world industrial processes, the difficulty of collecting data for different fault types varies, leading to imbalanced data, which poses significant obstacles to adversarial robust learning. Furthermore, it results in inaccurate boundary learning, poor robustness, and overfitting to the majority class in common empirical adversarial training methods. To overcome these problems, we propose a balanced adversarial learning strategy. On the one hand, the balanced generalization processing penalizes the imbalance in the generation of adversarial variant in inner training, and calibrates the confidence using the class-aware label smoothing in outer training, so as to learn more accurate boundaries with better game relations. On the other hand, it involves robust regularization processing, which adopts the smooth augmentation of adversarial examples in the input and the addition of data-related soft label regularization to learn good generalization with enriched information. The proposed method is validated on two industrial fault classification benchmarks with imbalanced data, and comparisons with related methods demonstrate that our approach achieves better standard and robust performance.