# Explainable Anomaly Detection for Industrial IoT Data Streams

arXiv: https://arxiv.org/abs/2512.08885 · HTML: https://mlsanity.com/paper/2512.08885/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 9 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?](https://mlsanity.com/paper/2412.14232/) (2024)
- [DFDT: Dynamic Fast Decision Tree for IoT Data Stream Mining on Edge Devices](https://mlsanity.com/paper/2502.14011/) (2025)
- [Online hierarchical partitioning of the output space in extreme multi-label data stream](https://mlsanity.com/paper/2507.20894/) (2025)
- [iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios](https://mlsanity.com/paper/2306.07775/) (2023)
- [Continual learning for rotating machinery fault diagnosis with cross-domain environmental and operational variations](https://mlsanity.com/paper/2504.10151/) (2025)

## Built on by (most cited first, 4 of 4)

- [Bridging Streaming Continual Learning via In-Context Large Tabular Models](https://mlsanity.com/paper/2512.11668/) (2025)
- [Axle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring](https://mlsanity.com/paper/2602.16101/) (2026)
- [Streaming Hierarchical Inference with Tabular Foundation Models](https://mlsanity.com/paper/2609.07956/) (2026)
- [Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus](https://mlsanity.com/paper/2609.23883/) (2026)

## Abstract (arXiv)

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.