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首页> 外文期刊>European Journal of Medicinal Chemistry: Chimie Therapeutique >Root-cause analysis for time-series anomalies via spatiotemporal graphical modeling in distributed complex systems
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Root-cause analysis for time-series anomalies via spatiotemporal graphical modeling in distributed complex systems

机译：分布式复杂系统时代天空图形建模的时序异常的根本原因分析

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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.
机译：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，因为操作模式多种多样，数据类型各异，故障传播机制复杂。本文提出了一种新的数据驱动的根本原因分析框架，该框架基于基于符号动力学概念的时空图形建模方法，用于发现和表示复杂CPS子系统之间的因果交互。我们通过提出的基于推理的度量将根本原因分析问题描述为最小化问题，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器的自由能概念，RBM）和人工异常关联（a（3），一种使用深度神经网络的分类框架，DNN）。通过模拟故障模式和异常节点的合成数据来验证所提出的方法。基于田纳西-伊斯曼过程（TEP）的真实数据集也用于与其他方法的比较。结果表明：（1）S-3和A（3）方法可以在基于模式和基于节点的故障场景下获得高精度的根本原因分析，此外，还可以成功处理多种标称操作模式；（2）所提出的工具链在保持高精度的同时具有可伸缩性，（3）与现有的方法相比，该框架在不同的故障条件下具有良好的鲁棒性和自适应性。（C） 2020爱思唯尔B.V.版权所有。

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