Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data

2026-09-12

AI Summary

本文提出基于最优传输的无监督异常检测框架，解决工业时序数据中异常检测问题，无需标签训练，适应动态数据，有效减少误报。

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.

Problem

• Anomaly Detection
• Optimal Transport
• Unsupervised Learning
• Industrial Data

Innovation

• Optimal Transport
• Unsupervised Anomaly Detection
• Counterfactual Explanations
• Sliding Reference Window