提出面向工业自动化时序异常检测的联邦学习数据集与评估框架。
Federated Learning for Multivariate Time Series Anomaly Detection in Industrial Automation
- 构建含周期性动态的工业时序数据集，模拟离散自动化过程。
- 在自建与公开数据集上验证多种异常检测方法性能差异。
- 解决数据规模、标注精度与共性缺陷问题，适合工业场景研究者。
联邦学习（FL）为多变量时间序列异常检测（MTSAD）开辟了新前景。然而，在联邦学习范式下进行此类检测方法的基准测试面临数据层面的挑战：现有数据集无法同时满足足够规模、准确标签以及规避常见缺陷的要求。此外，离散工业自动化中普遍存在的周期性过程行为在当前研究中尚未得到充分探索。本文旨在填补这一空白，通过设计一个基于离散自动化重复性特征的周期性动态数据集，并在该数据集及一个公开基准数据集上评估选定的MTSAD方法，推动相关研究进展。
原文摘要 · Abstract (English)
Federated learning (FL) has broadened the horizon for multivariate time series anomaly detection (MTSAD). However, benchmarking such anomaly detection methods within FL paradigm poses data-centric challenges. The existing datasets do not counteract these challenges since they do not simultaneously provide sufficient scale, accurate labels, and freedom from common flaws. In addition, the role of cyclic process behavior, which is common in discrete industrial automation, remains underexplored for MTSAD for the current state of research. This paper aims to shed more light on the literature and address these gaps by introducing a dataset designed with cyclic dynamics arising from the repetitive nature of discrete automation processes and evaluates selected MTSAD methods on both the proposed dataset and a public benchmark dataset.
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