工业物联网数据流异常检测，结合无监督算法与人机协作解释。
Explainable Anomaly Detection for Industrial IoT Data Streams
- 用在线孤立森林做无监督异常检测，实时处理数据流。
- 通过增量部分依赖图和特征重要性评分，动态解释异常原因。
- 适合需要实时故障预警与可解释性的工业维护场景。
工业维护正因物联网与边缘计算而变革，产生持续的数据流，需在计算资源有限的条件下实现实时、自适应决策。尽管数据流挖掘（DSM）可应对该挑战，但多数方法假设完全监督，而实际中真实标签常延迟或不可得。本文提出一种协作式DSM框架，融合无监督异常检测与人机交互学习以支持维护决策。采用在线孤立森林，并通过增量部分依赖图与特征重要性分数增强可解释性——该分数基于个体条件期望曲线偏离衰减均值的偏差计算，使用户可动态重评特征相关性并调整异常阈值。我们描述了实时实现方案，并提供雅卡尔织机单元故障检测的初步结果。后续工作聚焦于连续监测，以预测并解释即将发生的轴承故障。
原文摘要 · Abstract (English)
Industrial maintenance is being transformed by the Internet of Things and edge computing, generating continuous data streams that demand real-time, adaptive decision-making under limited computational resources. While data stream mining (DSM) addresses this challenge, most methods assume fully supervised settings, yet in practice, ground-truth labels are often delayed or unavailable. This paper presents a collaborative DSM framework that integrates unsupervised anomaly detection with interactive, human-in-the-loop learning to support maintenance decisions. We employ an online Isolation Forest and enhance interpretability using incremental Partial Dependence Plots and a feature importance score, derived from deviations of Individual Conditional Expectation curves from a fading average, enabling users to dynamically reassess feature relevance and adjust anomaly thresholds. We describe the real-time implementation and provide initial results for fault detection in a Jacquard loom unit. Ongoing work targets continuous monitoring to predict and explain imminent bearing failures.
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