yes / 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 the 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.

The complexity of engineered systems has increased remarkably to meet customer needs. In the continuously growing global market, it is essential for engineered systems to keep their productivities which can be achieved by higher reliability and availability. Integrated health management based on diagnostics and prognostics provides significant benefits, which includes increasing system safety and operational reliability, with a significant impact on the life-cycle costs, reducing operating costs and increasing revenues. Characteristics of complex systems such as nonlinearity, dynamicity, non-stationarity, and non-Gaussianity make diagnostics and prognostics more challenging tasks and decrease the application of classic reliability methods remarkably – as they cannot address the dynamic behaviour of these systems. This research has focused on detecting, predicting and isolating faults in engineered systems, using operational data with multifarious data characteristics. Complexities in the data, including non-Gaussianity and high nonlinearity, impose stringent challenges on fault analysis. To deal with these challenges, this research proposed an integrated data-driven methodology in which hidden Markov modelling (HMM) and Bayesian network (BN) were employed to detect, predict and isolate faults in a system. The fault detection and prediction were based on comparing and exploiting pattern similarity in the data via the loglikelihood values generated through HMM training. To identify the root cause of the faults, the probability values obtained from updating the BN were used which were based on the virtual evidence provided by HMM training and log-likelihood values. To set up a more accurate data-driven model – particularly BN structure – engineering analyses were employed in a structured way to explore the causal relationships in the system which is essential for reliability analysis of complex engineered systems. The automotive exhaust gas Aftertreatment system is a complex engineered system consisting of several subsystems working interdependently to meet emission legislations. The Aftertreatment system is a highly nonlinear, dynamic and non-stationary system. Consequently, it has multifarious data characteristics, where these characteristics raise the challenges of diagnostics and prognostics for this system, compared to some of the references systems, such as the Tennessee Eastman process or rolling bearings. The feasibility and effectiveness of the presented framework were discussed in conjunction with the application to a real-world case study of an exhaust gas Aftertreatment system which provided good validation of the methodology, proving feasibility to detect, predict, and isolate unidentified faults in dynamic processes.