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arXiv · 2609.13940

Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data

Abigail Langbridge·Fearghal O'Donncha·James T Rayfield·Bradley Eck
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Abstract

Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based framework for anomaly detection, designed to detect deviations from normal behaviour in time-series sensor data. The OT-based method requires minimal user input and adapts to real-time data without the need for labelled training data. Our method effectively addresses existing limitations related to data labelling, generalisability, and scalability, demonstrating resilience against short-term fluctuations, noise, and data gaps - common challenges in industrial environments. Additionally, our method provides counterfactual explanations improving the auditability of the approach when deployed in industrial settings. The proposed method learns the mapping between normal and observed operating conditions through a sliding reference window that adapts to the dynamicity of the data. We evaluate our approach on three industrial datasets, from shipping, industrial HVAC systems, and publicly available benchmark data. The method was highly effective in identifying anomalies and reducing false positives, outperforming traditional methods, while maintaining computational efficiency and ease of configuration.

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Abigail Langbridge, Fearghal O'Donncha, James T Rayfield, Bradley Eck. 2026-09-12. Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data. https://doi.org/10.1109/bigdata62323.2024.10825081

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