For explainable fault detection and classification (FDC), this paper proposes a unified framework, ABIGX (Adversarial fault reconstruction-Based Integrated Gradient eXplanation). ABIGX is derived from the essentials of previous successful fault diagnosis methods, contribution plots (CP) and reconstruction-based contribution (RBC). It is the first explanation framework that provides variable contributions for the general FDC models. The core part of ABIGX is the adversarial fault reconstruction (AFR) method, which rethinks the FR from the perspective of adversarial attack and generalizes to fault classification models with a new fault index. For fault classification, we put forward a new problem of fault class smearing, which intrinsically hinders the correct explanation. We prove that ABIGX effectively mitigates this problem and outperforms the existing gradient-based explanation methods. For fault detection, we theoretically bridge ABIGX with conventional fault diagnosis methods by proving that CP and RBC are the linear specifications of ABIGX. The experiments evaluate the explanations of FDC by quantitative metrics and intuitive illustrations, the results of which show the general superiority of ABIGX to other advanced explanation methods.

提出了一种统一框架ABIGX (Adversarial fault reconstruction-Based Integrated Gradient eXplanation)用于可解释性故障检测和分类（FDC），该框架结合了贡献图（CP）和基于重构的贡献（RBC）等成功的故障诊断方法的要点，通过故障重构的对抗方法（AFR）理念解决了故障分类中的类染问题，实验证明ABIGX相比现有的基于梯度的方法在FDC的解释性方面更为优越。

ABIGX：可解释故障检测与分类的统一框架