最优传输用于工业数据的高效无监督异常检测
Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data
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中文总结 AI 辅助
本文提出基于最优传输的无监督异常检测框架，无需标签即可适应实时工业传感器数据，在航运、暖通空调及基准数据集上有效识别异常并减少误报，优于传统方法。
中文摘要 AI 辅助
有效的异常检测框架是工业4.0范式的核心支柱。在本文中，我们介绍了一种基于最优传输（OT）的异常检测框架，旨在检测时间序列传感器数据中偏离正常行为的偏差。该基于OT的方法需要最少的用户输入，并且无需标记训练数据即可适应实时数据。我们的方法有效解决了与数据标记、泛化性和可扩展性相关的现有局限性，展示了对短期波动、噪声和数据缺失的鲁棒性——这些是工业环境中的常见挑战。此外，我们的方法提供了反事实解释，提高了该方法在工业环境中部署时的可审计性。所提出的方法通过一个滑动参考窗口学习正常操作条件与观测操作条件之间的映射，该窗口适应数据的动态性。我们在三个工业数据集上评估了我们的方法，这些数据集来自航运、工业暖通空调系统以及公开可用的基准数据。该方法在识别异常和减少误报方面非常有效，优于传统方法，同时保持了计算效率和配置的简便性。
英文摘要
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.
发表机构
- Imperial College London（帝国理工学院）
- IBM Research Europe（IBM欧洲研究院）
- IBM T.J. Watson Research Center（IBM T.J.沃森研究中心）
机构由 AI 辅助整理，请以论文原文为准。
