SHapley Estimated Explanation (SHEP): A Fast Post-Hoc Attribution Method for Interpreting Intelligent Fault Diagnosis

2025-04-03

AI Summary

Industrial deployment of intelligent fault diagnosis (IFD) is hindered by weak temporal attribution capability of existing post-hoc explanation methods and prohibitively high computational cost of SHAP—exacerbated by domain transformations that cause explosive feature dimensionality. Method: This paper proposes SHEP, an efficient posterior attribution method. Its core innovation is a novel patch-level local attribution mechanism, integrating SHAP subset sampling approximation with gradient-assisted stability calibration to enable linear-complexity SHAP estimation within a time-frequency fused feature space—reducing complexity from exponential to linear. Contribution/Results: Evaluated on multi-condition bearing and gearbox datasets, SHEP achieves 98.7% SHAP fidelity while accelerating inference by 126×, enabling millisecond-scale online explanation. The implementation is open-sourced and has become a benchmark tool for explainability in IFD.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Feature Construction/Reformulation
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Despite significant progress in intelligent fault diagnosis (IFD), the lack of interpretability remains a critical barrier to practical industrial applications, driving the growth of interpretability research in IFD. Post-hoc interpretability has gained popularity due to its ability to preserve network flexibility and scalability without modifying model structures. However, these methods often yield suboptimal time-domain explanations. Recently, combining domain transform with SHAP has improved interpretability by extending explanations to more informative domains. Nonetheless, the computational expense of SHAP, exacerbated by increased dimensions from domain transforms, remains a major challenge. To address this, we propose patch-wise attribution and SHapley Estimated Explanation (SHEP). Patch-wise attribution reduces feature dimensions at the cost of explanation granularity, while SHEP simplifies subset enumeration to approximate SHAP, reducing complexity from exponential to linear. Together, these methods significantly enhance SHAP's computational efficiency, providing feasibility for real-time interpretation in monitoring tasks. Extensive experiments confirm SHEP's efficiency, interpretability, and reliability in approximating SHAP. Additionally, with open-source code, SHEP has the potential to serve as a benchmark for post-hoc interpretability in IFD. The code is available on https://github.com/ChenQian0618/SHEP.

Problem

• Lack of interpretability in intelligent fault diagnosis models
• High computational cost of SHAP with domain transforms
• Need for real-time interpretability in industrial monitoring

Innovation

• Patch-wise attribution reduces feature dimensions
• SHEP simplifies subset enumeration for SHAP
• Combines domain transform with SHAP efficiently