ABIGX: A Unified Framework for eXplainable Fault Detection and
  Classification
Yue Zhuo Jinchuan Qian Zhihuan Song Zhiqiang Ge
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.