用AI解释方法定位化工故障根源，提升模型可信度
Explainability for Fault Detection System in Chemical Processes
- 对比IG与SHAP两种可解释AI方法，分析LSTM故障诊断决策
- 多数情况下关键特征一致，部分场景SHAP更贴近故障源头
- 方法通用性强，适用于类似工业过程的故障检测
本文应用并比较了两种先进的可解释人工智能（XAI）方法——集成梯度（IG）和SHAP值（SHapley Additive exPlanations），用于解释一个高精度长短期记忆（LSTM）分类器在基准非线性化工过程——田纳西东曼过程（TEP）中的故障诊断决策。结果表明，XAI方法能有效识别故障发生的工艺子系统。基于工艺知识，我们发现多数情况下两类方法识别的关键特征一致；但在某些场景下，SHAP方法表现更优，更接近故障根本原因。由于所用XAI方法具有模型无关性，该方法不仅适用于当前过程，还可推广至其他类似工业问题。
原文摘要 · Abstract (English)
In this work, we apply and compare two state-of-the-art eXplainability Artificial Intelligence (XAI) methods, the Integrated Gradients (IG) and the SHapley Additive exPlanations (SHAP), that explain the fault diagnosis decisions of a highly accurate Long Short-Time Memory (LSTM) classifier. The classifier is trained to detect faults in a benchmark non-linear chemical process, the Tennessee Eastman Process (TEP). It is highlighted how XAI methods can help identify the subsystem of the process where the fault occurred. Using our knowledge of the process, we note that in most cases the same features are indicated as the most important for the decision, while insome cases the SHAP method seems to be more informative and closer to the root cause of the fault. Finally, since the used XAI methods are model-agnostic, the proposed approach is not limited to the specific process and can also be used in similar problems.
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