用可解释AI发现工业系统模型缺陷并提升预测性能
Explainable AI to Improve Machine Learning Reliability for Industrial Cyber-Physical Systems
- 用SHAP值分析时序分解成分对预测的影响
- 发现训练数据上下文不足导致性能下降
- 根据XAI结果扩大数据窗口，提升模型可靠性
工业网络物理系统（CPS）在安全与经济上均具敏感性，其可靠性至关重要。机器学习（ML），尤其是深度学习，正日益融入工业CPS，但模型的内在复杂性导致决策过程不透明。为防止模型在未见数据上出现意外行为，需进行严格评估。可解释人工智能（XAI）可用于揭示模型推理逻辑，实现更全面的行为分析。本文将XAI应用于改进工业CPS场景下机器学习模型的预测性能。通过分析时序数据分解组件对预测的影响，利用SHAP值发现模型训练中缺乏足够上下文信息。基于XAI结果，扩大数据实例的时间窗口，有效提升了模型表现。
原文摘要 · Abstract (English)
Industrial Cyber-Physical Systems (CPS) are sensitive infrastructure from both safety and economics perspectives, making their reliability critically important. Machine Learning (ML), specifically deep learning, is increasingly integrated in industrial CPS, but the inherent complexity of ML models results in non-transparent operation. Rigorous evaluation is needed to prevent models from exhibiting unexpected behaviour on future, unseen data. Explainable AI (XAI) can be used to uncover model reasoning, allowing a more extensive analysis of behaviour. We apply XAI to improve predictive performance of ML models intended for an industrial CPS use-case. We analyse the effects of components from time-series data decomposition on model predictions using SHAP values. Through this method, we observe evidence on the lack of sufficient contextual information during model training. By increasing the window size of data instances, informed by the XAI findings for this use-case, we are able to improve model performance.
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