发表机构
GECAD, ISEP, Polytechnic of Porto; SISTRADE Software Consulting（波尔图理工学院，ISEP，GECAD; SISTRADE软件咨询公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该系统通过异构检测器加权共识与替代模型解释，将人类分析师纳入决策循环，在工业数据流上实现稳健异常检测与可操作交互。
AI 中文摘要
我们提出了一种面向高速数据流的协作式流异常检测系统，该系统将人类分析师明确地整合到决策循环中。系统结合了异构检测器，并通过基于归一化的加权共识聚合其输出，辅以工件感知规则，以稳定部署下的异常评分。为提高可解释性，系统推导出近似集成共识的替代模型，并揭示与异常行为相关的可读传感器条件。分析师可以通过审查异常事件、调整共识行为以及细化用于异常预测的替代规则来主动干预，从而产生人工调整的集成。我们在包含260,000个事件和3个异常事件的工业数据流上评估了该方法，展示了稳健的检测性能和可操作的人机交互。
英文摘要
We present a collaborative streaming anomaly detection system for high-speed data streams that explicitly integrates human analysts into the decision loop. The system combines heterogeneous detectors and aggregates their outputs through a normalization-based weighted consensus, complemented by artifact-aware rules to stabilize anomaly scoring under deployment. To improve interpretability, it derives surrogate models that approximate the ensemble consensus and expose human-readable sensor conditions associated with anomalous behavior. Analysts can actively intervene by reviewing anomaly episodes, adjusting consensus behavior, and refining surrogate rules used for anomaly prediction, producing a human-adjusted ensemble. We evaluate the approach on an industrial stream with 260\,000 events and 3 anomalous episodes, showing robust detection and actionable human-AI interaction.
CommentsStreaming Continual Learning ECML PKDD Workshop 2026
