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This work was undertaken as a final year project in Computer Engineering, within the Department of Computer and Information Science at Linköping University. At the Department of Computer and Information Science, work oriented at testing and analyzing applications is developed to provide solution approaches to problems that arise in system product development. One of the current applications being developed is the ‘Systemics Analyst’. The purpose of the application is to facilitate for system developers with an analysis tool permitting insights on system reliability, system critical components, how to improve the system and the consequences as well as risks of a system failure. The purpose of the present thesis was to enhance the ‘Systemics Analyst application’ by incorporating an ‘automated model-based reliability prediction’ and ‘fault tree analysis’ modules. This enables reliability prediction and fault tree analysis diagrams to be generated automatically from the data files and relieves the system developer from manual creation of the diagrams. The enhanced Systemics Analyst application managed to present the results in respective models using the new incorporated functionality. To accomplish the above tasks, ‘Systemics Analyst application’ was integrated with a library that handles automated model-based reliability prediction and fault tree analysis, which is described in this thesis. The reader will be guided through the steps that are performed to accomplish the tasks with illustrating figures, methods and code examples in order to provide a closer vision of the work performed.

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Low volume high value (LVHV) supply chains such as airline manufacturing, power plant construction, and shipbuilding are especially susceptible to risks. These industries are characterized by long lead times and a limited number of suppliers that have both the technical know-how and manufacturing capabilities to deliver the requisite goods and services. Disruptions within the supply chain are common and can cause significant and costly delays. Although supply chain risk management and supply chain reliability are topics that have been studied extensively, most research in these areas focus on high vol- ume supply chains and few studies proactively identify risks. In this research, we develop methodologies to proactively and quantitatively identify and mitigate supply chain risks within LVHV supply chains. First, we propose a framework to model the supply chain system using fault-tree analysis based on the bill of material of the product being sourced. Next, we put forward a set of mathematical optimization models to proactively identify, mitigate, and resource at-risk suppliers in a LVHV supply chain with consideration for a firm’s budgetary constraints. Lastly, we propose a machine learning methodology to quan- tify the risk of an individual procurement using multiple logistic regression and industry available data, which can be used as the primary input to the fault tree when analyzing overall supply chain system risk. Altogether, the novel approaches proposed within this dissertation provide a set of tools for industry practitioners to predict supply chain risks, optimally choose which risks to mitigate, and make better informed decisions with respect to supplier selection and risk mitigation while avoiding costly delays due to disruptions in LVHV supply chains.

Yes / Time, cost and risk are the main elements that effect the operating margin of the oil and gas companies due to Turnaround Maintenance (TAM). Turnaround Maintenance (TAM) is a methodology for the total shutdown of plant facilities during a pre-defined period to execute inspection actions, replacement and repairs according to Scope of Work (SoW). This paper presents a new methodology for improving TAM scheduling of oil and gas plants. The methodology includes four stages: removing Non-critical Equipment (NE) from reactive maintenance to proactive maintenance, risk-based inspection of Critical Static Equipment (CSE), risk-based failure of Critical Rotating Equipment (CRE), and application of failure distributions. The results from improving TAM scheduling is associated with decreasing duration and increasing interval between TAM leading to improved availability, reliability, operation and maintenance costs and safety risks. The paper presents findings from the TAM model application. The methodology is fairly generic in its approach and can also be adapted for implementation in other oil and gas industries that operate under similar harsh conditions.

Yes / Reliability evaluation for ensuring the uninterrupted system operation is an integral part of dependable system development. Model-based safety analysis (MBSA) techniques such as Hierarchically Performed Hazard Origin and Propagation Studies (HiP-HOPS) have made the reliability analysis process less expensive in terms of effort and time required. HiP-HOPS uses an analytical modelling approach for Fault tree analysis to automate the reliability analysis process, where each system component is associated with its failure rate or failure probability. However, such non-state-space analysis models are not capable of modelling more complex failure behaviour of component like failure/repair dependencies, e.g., spares, shared repair, imperfect coverage, etc. State-space based paradigms like Markov chain can model complex failure behaviour, but their use can lead to state-space explosion, thus undermining the overall analysis capacity. Therefore, to maintain the benefits of MBSA while not compromising on modelling capability, in this paper, we propose a conceptual framework to incorporate complex basic events in HiP-HOPS. The idea is demonstrated via an illustrative example. / DEIS H2020 Project under Grant 732242.

Yes / Quantitative risk assessment (QRA) approaches systematically evaluate the likelihood, impacts, and risk of adverse events. QRA using fault tree analysis (FTA) is based on the assumptions that failure events have crisp probabilities and they are statistically independent. The crisp probabilities of the events are often absent, which leads to data uncertainty. However, the independence assumption leads to model uncertainty. Experts’ knowledge can be utilized to obtain unknown failure data; however, this process itself is subject to different issues such as imprecision, incompleteness, and lack of consensus. For this reason, to minimize the overall uncertainty in QRA, in addition to addressing the uncertainties in the knowledge, it is equally important to combine the opinions of multiple experts and update prior beliefs based on new evidence. In this article, a novel methodology is proposed for QRA by combining fuzzy set theory and evidence theory with Bayesian networks to describe the uncertainties, aggregate experts’ opinions, and update prior probabilities when new evidences become available. Additionally, sensitivity analysis is performed to identify the most critical events in the FTA. The effectiveness of the proposed approach has been demonstrated via application to a practical system. / The research of Sohag Kabir was partly funded by the DEIS project (Grant Agreement 732242).