[论文解读] Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams

Natalia Wojak-Strzelecka, Szymon Bobek+2 more|arXiv (Cornell University)|Mar 9, 2026

Time Series Analysis and Forecasting被引用 0

一句话总结

该论文通过结合改进的 Page-Hinkley 变点检测器、用于异常检测的在线领域自适应，以及帮助人类操作员的可解释性 AI 组件，提出在工业数据流中区分故障与健康领域漂移的方法。

ABSTRACT

Anomaly and failure detection methods are crucial in identifying deviations from normal system operational conditions, which allows for actions to be taken in advance, usually preventing more serious damages. Long-lasting deviations indicate failures, while sudden, isolated changes in the data indicate anomalies. However, in many practical applications, changes in the data do not always represent abnormal system states. Such changes may be recognized incorrectly as failures, while being a normal evolution of the system, e.g. referring to characteristics of starting the processing of a new product, i.e. realizing a domain shift. Therefore, distinguishing between failures and such ''healthy'' changes in data distribution is critical to ensure the practical robustness of the system. In this paper, we propose a method that not only detects changes in the data distribution and anomalies but also allows us to distinguish between failures and normal domain shifts inherent to a given process. The proposed method consists of a modified Page-Hinkley changepoint detector for identification of the domain shift and possible failures and supervised domain-adaptation-based algorithms for fast, online anomaly detection. These two are coupled with an explainable artificial intelligence (XAI) component that aims at helping the human operator to finally differentiate between domain shifts and failures. The method is illustrated by an experiment on a data stream from the steel factory.

研究动机与目标

• 需要阐明在工业数据流中将健康的领域漂移与真实故障分离的必要性。
• 开发一种方法，检测分布变化、在线自适应异常检测模型，并向人类操作员解释差异。
• 以钢材冷轧工艺的案例研究证明可行性。

提出的方法

• 使用经对参考分布与近似分布之间 KL 散度增强的改进 Page-Hinkley 变点检测器来识别分布变化。
• 在小批量数据中使用领域自适配分类器（CCSA）将异常检测从源域自适应到目标域。
• 应用 SHAP 解释来比较领域自适应前后的特征重要性，以帮助人类区分领域漂移和故障。

实验结果

研究问题

• RQ1 工业数据流中的分布变化是否能够准确地分离为健康的领域漂移与故障？
• RQ2 在线领域自适应结合可解释 AI 是否比标准异常/故障检测器更能提升区分效果？
• RQ3 基于 SHAP 的解释在领域自适应步骤中如何演变，以支持人类决策？

主要发现

• 所提出的方法能够在域漂移与故障之间实现区分，超出常规异常检测器的能力。
• 使用 CCSA 的领域自适应使在少量目标域样本下的学习变得有效。
• SHAP 解释揭示了自适应过程中的特征重要性变化，帮助操作员将故障与领域漂移区分开。
• 钢厂冷轧轧机数据集上的实验表明可检测的漂移及相应解释。