arXiv 2404.082242024-04-18cs.LGcs.AIcs.CRcs.ITcs.SYeess.SYmath.IT

HCL-MTSAD：用于工业多变量时间序列异常准确检测的层次对比一致性学习

HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate Detection of Industrial Multivariate Time Series Anomalies

Haili Sun, Yan Huang, Lansheng Han, Cai Fu, Chunjie Zhou

备注：: This paper is a manuscript that is still in the process of revision, including Table 1, Figure 2, problem definition in section III.B and method description proposed in section IV. In addition, the submitter has not been authorized by the first author and other co-authors to post the paper to arXiv

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AI总结：

提出HCL-MTSAD，一种自监督层次对比一致性学习方法，通过多层次一致性捕获和对比损失挖掘时空关联，在六个工业数据集上平均F1提升1.8%。

摘要：

多变量时间序列（MTS）异常检测旨在识别偏离标准运行模式的样本，这对于确保工业应用的安全性和可靠性至关重要。该领域的主要挑战是开发能够有效辨别异常的表征。文献中流行的异常检测方法主要是基于重构和预测的。然而，它们通常集中于单维度的实例层面，从而未能充分利用工业MTS中固有的复杂关联。为解决此问题，我们提出了一种新颖的自监督层次对比一致性学习方法，用于检测MTS中的异常，命名为HCL-MTSAD。该方法创新性地利用工业MTS中固有的多层次数据一致性，系统性地捕获四个潜在层面（测量、样本、通道和过程）的一致关联。通过开发多层对比损失，HCL-MTSAD能够广泛挖掘数据一致性和时空关联，从而产生更具信息量的表征。随后，设计了一个基于自监督层次对比学习的异常判别模块，通过计算多尺度数据一致性来检测时间戳级别的异常。在从真实网络物理系统和服务器机器获取的六个不同MTS数据集上进行的广泛实验，与20个基线相比，表明HCL-MTSAD的异常检测能力在F1分数上平均优于最先进的基准模型1.8%。

英文摘要：

Multivariate Time Series (MTS) anomaly detection focuses on pinpointing samples that diverge from standard operational patterns, which is crucial for ensuring the safety and security of industrial applications. The primary challenge in this domain is to develop representations capable of discerning anomalies effectively. The prevalent methods for anomaly detection in the literature are predominantly reconstruction-based and predictive in nature. However, they typically concentrate on a single-dimensional instance level, thereby not fully harnessing the complex associations inherent in industrial MTS. To address this issue, we propose a novel self-supervised hierarchical contrastive consistency learning method for detecting anomalies in MTS, named HCL-MTSAD. It innovatively leverages data consistency at multiple levels inherent in industrial MTS, systematically capturing consistent associations across four latent levels-measurement, sample, channel, and process. By developing a multi-layer contrastive loss, HCL-MTSAD can extensively mine data consistency and spatio-temporal association, resulting in more informative representations. Subsequently, an anomaly discrimination module, grounded in self-supervised hierarchical contrastive learning, is designed to detect timestamp-level anomalies by calculating multi-scale data consistency. Extensive experiments conducted on six diverse MTS datasets retrieved from real cyber-physical systems and server machines, in comparison with 20 baselines, indicate that HCL-MTSAD's anomaly detection capability outperforms the state-of-the-art benchmark models by an average of 1.8\% in terms of F1 score.

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