Explainable Anomaly Detection for Industrial IoT Data Streams

• URL: http://arxiv.org/abs/2512.08885v1
• Date: Tue, 09 Dec 2025 18:20:35 GMT
• Title: Explainable Anomaly Detection for Industrial IoT Data Streams
• Authors: Ana Rita Paupério, Diogo Risca, Afonso Lourenço, Goreti Marreiros, Ricardo Martins,
• Abstract summary: Industrial maintenance is being transformed by the Internet of Things and edge computing.<n>This paper presents a framework that integrates unsupervised anomaly detection with interactive, human-in-the-loop learning to support maintenance decisions.
• Score: 1.2722697496405462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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