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Integration of Hidden Markov Modelling and Bayesian Network for fault detection and prediction of complex engineered systems

机译：隐马尔可夫建模与贝叶斯网络的集成故障检测与复杂工程系统的预测

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摘要

This paper presents a methodology for fault detection, fault prediction and fault isolation based on the integration of hidden Markov modelling (HMM) and Bayesian networks (BN). This addresses the nonlinear and non-Gaussian data characteristics to support fault detection and prediction, within an explainable hybrid framework that captures causality in a complex engineered system. The proposed methodology is based on the analysis of the pattern of similarity in the log-likelihood (LL) sequences against the training data for the mixture of Gaussians HMM (MoG-HMM). The BN model identifies the root cause of detected/predicted faults, using the information propagated from the HMM model as empirical evidence. The feasibility and effectiveness of the presented approach are discussed in conjunction with the application to a real-world case study of an automotive exhaust gas Aftertreatment system. The paper details the implementation of the methodology to this case study, with data available from real-world usage of the system. The results show that the proposed methodology identifies the fault faster and attributes the fault to the correct root cause. While the proposed methodology is illustrated with an automotive case study, its applicability is much wider to the fault detection and prediction problem of any similar complex engineered system.
机译：本文基于隐马尔可夫建模（HMM）和贝叶斯网络（BN）的集成，提出了一种故障检测，故障预测和故障隔离的方法。这解决了非线性和非高斯数据特性来支持故障检测和预测，在可说明的混合框架内，该框架在复杂的工程系统中捕获因果关系。所提出的方法基于对对Log-似然（LL）序列中的相似性模式的分析，针对高斯HMM（MOG-HMM）的混合物的训练数据。 BN模型识别检测/预测故障的根本原因，使用从HMM模型传播的信息作为经验证据。所提出的方法的可行性和有效性与应用于汽车废气后处理系统的真实案例研究。本文详细介绍了这种案例研究的方法，可从现实世界使用系统提供数据。结果表明，所提出的方法更快地标识了故障并将故障归因于正确的根本原因。虽然所提出的方法是用汽车案例研究说明的，但其适用性对于任何类似的复杂工程系统的故障检测和预测问题很宽。

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