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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.

Safety analysis is critical when developing new engineering systems. Many systems have to function under randomly occurring events, making stochastic processes useful in a safety modelling context. However, a general stochastic process is very challenging to analyse mathematically. Therefore, model restrictions are necessary to simplify the mathematical analysis. A popular simplified stochastic model is the Semi-Markov process (SMP), which is a generalization of the "memoryless" continuous-time Markov chain. However, only a subclass of Semi-Markov models can be analysed with non-simulation based methods. In these models, the cumulative density function (cdf) of the random variables describing the system is in the form of expolynomials. This thesis investigates the possibility to extend the number of Semi-Markov models that can be analysed with non-simulation based methods by approximating the non-expolynomial random variables with expolynomials. This thesis focus on approximation of models partially described by LogNormal and Weibull distributed random variables. The result shows that it is possible to approximate some Semi-Markov models with non-expolynomial random variables. However, there is an increasing difficulty in approximating a non-expolynomial random variable when the variability in the distribution increases. / Säkerhetsanalys är avgörande när man utvecklar nya tekniska system. Många system måste fungera under slumpmässigt inträffande händelser, vilket gör stokastiska processer användbara i ett säkerhetsmodellerande sammanhang. En allmän stokastisk process är dock mycket utmanande att analysera matematiskt. Därför är begränsningar på modellen nödvändiga för att förenkla den matematiska analysen. En populär förenklad stokastisk modell är Semi-Markov-processen (SMP), vilket är en generalisering av den "minneslösa" tids-kontinuerliga Markov-kedjan. Dock är det endast en underklass av Semi-Markov-modeller som kan analyseras med icke-simuleringsbaserade metoder. I dessa modeller är den kumulativa densitetsfunktionen (cdf) för de slumpmässiga variablerna som beskriver systemet i form av expolynomials. Denna rapport undersöker möjligheten att utöka antalet Semi-Markov-modeller som kan analyseras med icke-simuleringsbaserade metoder genom att approximera de icke-expolynomial slumpvariablerna med expolynomials. Vi fokuserar på approximering av modeller som delvis beskrivs av LogNormal distribuerade och Weibull distribuerade slumpmässiga variabler. Resultatet visar att det är möjligt att approximera vissa stokastiska variabler som är icke-expolynomial i Semi-Markov-modeller. Resultatet visar dock att det är en ökande svårighet att approximera en icke-expolynomial slumpmässiga variabeln när variabiliteten i fördelningen ökar.