Root-cause analysis for time-series anomalies via spatiotemporal graphical modeling in distributed complex systems
Root-cause analysis for time-series anomalies via spatiotemporal graphical modeling in distributed complex systems
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DOI：
10.1016/j.knosys.2020.106527
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发表时间：
2021-01-09
影响因子：
8.8
通讯作者：
Sarkar, Soumik
中科院分区：
文献类型：
--
作者：
Liu, Chao;Lore, Kin Gwn;Sarkar, Soumik
由于操作模式广泛多样、数据类型不同以及复杂的故障传播机制，复杂网络物理系统 (CPS) 中的性能监控、异常检测和根本原因分析通常非常棘手。本文提出了一种新的数据驱动根本原因分析框架，该框架基于时空图形建模方法，该方法基于符号动力学概念，用于发现和表示复杂 CPS 子系统之间的因果相互作用。我们通过所提出的基于推理的度量将根本原因分析问题表述为最小化问题，并提出了两种近似的根本原因分析方法，即顺序状态切换（S-3，基于受限玻尔兹曼机的自由能概念，RBM）和人工异常关联（A(3)，使用深度神经网络的分类框架，DNN）。对来自失败模式和异常节点案例的合成数据进行模拟，以验证所提出的方法。基于田纳西伊士曼过程（TEP）的真实数据集也用于与其他方法进行比较。结果表明：（1）S-3和A（3）方法除了成功处理多种标称操作模式外，在基于模式和基于节点的故障场景下都可以获得高精度的根本原因分析，（2）所提出的工具链在保持高精度的同时具有可扩展性，（3）所提出的框架在不同的故障条件下具有鲁棒性和适应性，并且与最先进的方法相比表现更好。 (C) 2020 Elsevier B.V. 保留所有权利。
Performance monitoring, anomaly detection, and root-cause analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, disparate data types, and complex fault propagation mechanisms. This paper presents a new data-driven framework for root-cause analysis, based on a spatiotemporal graphical modeling approach built on the concept of symbolic dynamics for discovering and representing causal interactions among sub-systems of complex CPSs. We formulate the root-cause analysis problem as a minimization problem via the proposed inference based metric and present two approximate approaches for root-cause analysis, namely the sequential state switching (S-3, based on free energy concept of a restricted Boltzmann machine, RBM) and artificial anomaly association (A(3), a classification framework using deep neural networks, DNN). Synthetic data from cases with failed pattern(s) and anomalous node(s) are simulated to validate the proposed approaches. Real dataset based on Tennessee Eastman process (TEP) is also used for comparison with other approaches. The results show that: (1) S-3 and A(3) approaches can obtain high accuracy in root-cause analysis under both pattern-based and node-based fault scenarios, in addition to successfully handling multiple nominal operating modes, (2) the proposed tool-chain is shown to be scalable while maintaining high accuracy, and (3) the proposed framework is robust and adaptive in different fault conditions and performs better in comparison with the state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved.