Challenges and Opportunities of XAI in Industrial Intelligent Diagnosis:Attribution Interpretation

The purpose is to figure the lack of interpretability for current industrial intelligence diagnosis methods, review the development situation of model-agnostics attribution analysis in industrial intelligence diagnosis and point out the potential development direction. The main viewpoints and functions of interpretable techniques are analyzed. Aiming at two characteristic problems of industrial intelligence diagnosis, i.e., nonlinear high-dimensional observation and inaccurate knowledge representation,attribution interpretation provides effective methods for understanding forward logical structure and reverse optimizing design of intelligent models. The core concepts, existing works and pros and cons of attention mechanism, saliency analysis, rule extraction, and proxy model are systematically summarized. Four case studies are used to illustrate the result of attribution interpretation techniques.Finally, potential research directions of attribution interpretation technology in industrial intelligent diagnosis are discussed, including quantification of interpretability, feedback to model design, balance between model complexity and interpretability, and attribution analysis in high dimension. Through this review, we hope to provide a suggestion to conduct further development of interpretable intelligence in industrial fault diagnosis.