In 2004, the NASA Astronaut Office produced a memo regarding the safety of next generation launch vehicles. The memo requested that these vehicles have a probability of loss of crew of at most 1 in 1000 flights, which represents nearly an order of magnitude decrease from current vehicles. The goal of LOC of 1 in 1000 flights has since been adopted by the launch vehicle design community as a requirement for the safety of future vehicles. This research addresses the gap between current vehicles and future goals by improving the capture of vehicle architecture effects on reliability and safety. Vehicle architecture pertains to the physical description of the vehicle itself, which includes manned or unmanned, number of stages, number of engines per stage, engine cycle types, redundancy, etc. During the operations phase of the vehicle life-cycle it is clear that each of these parameters will have an inherent effect on the reliability and safety of the vehicle. However, the vehicle architecture is typically determined during the early conceptual design phase when a baseline vehicle is selected. Unless a great amount of money and effort is spent, the architecture will remain relatively constant from conceptual design through operations. Due to the fact that the vehicle architecture is essentially “locked-in” during early design, it is expected that much of the vehicle's reliability potential will also be locked-in. This observation leads to the conclusion that improvement of vehicle reliability and safety in the area of vehicle architecture must be completed during early design. Evaluation of the effects of different architecture decisions must be performed prior to baseline selection, which helps to identify a vehicle that is most likely to meet the reliability and safety requirements when it reaches operations. Although methods exist for evaluating reliability and safety during early design, weaknesses exist when trying to evaluate all architecture effects simultaneously. The goal of this research was therefore to formulate and implement a method that is capable of quantitatively evaluating vehicle architecture effects on reliability and safety during early conceptual design. The ConcepTual Reliability Growth Approach for CompariSon of Launch Vehicle ArchiTectures (CONTRAST) was developed to meet this goal. Using the strengths of existing techniques a hybrid approach was developed, which utilizes a reliability growth projection to evaluate the vehicles. The growth models are first applied at the subsystem level and then a vehicle level projection is generated using a simple system level fault tree. This approach allows for the capture of all trades of interest at the subsystem level as well as many possible trades at the assembly level. The CONTRAST method is first tested on an example problem, which compares the method output to actual data from the Space Transportation System (STS). This example problem illustrates the ability of the CONTRAST method to capture reliability growth trends seen during vehicle operations. It also serves as a validation for the development of the reliability growth model assumptions for future applications of the method. The final chapter of the thesis applies the CONTRAST method to a relevant launch vehicle, the Space Launch System (SLS), which is currently under development. Within the application problem, the output of the method is first used to check that the primary research objective has been met. Next, the output is compared to a state-of-the-art tool in order to demonstrate the ability of the CONTRAST method to alleviate one of the primary consequences of using existing techniques. The final section within this chapter presents an analysis of the booster and upper stage block upgrade options for the SLS vehicle. A study of the upgrade options was carried out because the CONTRAST method is uniquely suited to look at the effects of such strategies. The results from the study of SLS block upgrades give interesting observations regarding the desired development order and upgrade strategy. Ultimately this application problem demonstrates the merits of applying the CONTRAST method during early design. This approach provides the designer with more information in regard to the expected reliability of the vehicle, which will ultimately enable the selection of a vehicle baseline that is most likely to meet the future requirements.

Risk management is essential for the construction industry to successfully fulfill project objectives. Several studies were conducted in the past decade to support quantitative risk analysis. These studies were based on using some of the commonly used techniques such as risk matrix, decision trees, Monte Carlo, and sensitivity analysis. However, some of these techniques are limited because they either do not support quantitative risk analysis, or are difficult to be utilized due to the required amount of data to support quantitative risk analysis. To address such limitations, a comprehensive framework was developed, based on combining three well-known techniques in reliability engineering, i.e., failure mode and effect analysis, fault trees, and event trees with fuzzy logic. Fuzzy logic and failure mode and effect analysis were first combined to provide an answer to the problem of identifying of critical risk events through the development of a fuzzy expert system software package named Risk Criticality Analyzer. To support quantitative risk analysis in the construction industry, fault tree and event tree were combined, and fuzzy logic is used to solve both of them. Fuzzy arithmetic operations on fuzzy numbers were used to represent logical gates in the fault tree structure, and to conduct event tree analysis. To automate solving both fault trees and event trees, Fuzzy Reliability Analyzer was designed and implemented using Visual Basic.net. Both tools were then validated through case studies. The results indicate that by using the proposed methodology, the risk can be assessed effectively and efficiently. The proposed framework presented in this research provides the contribution of combining fuzzy logic with failure mode and effect analysis, fault trees, and event trees in a comprehensive framework to support risk identification, risk assessment, and risk response. Since the proposed framework is based on using linguistic terms, risk analysts are offered a more convenient and practical framework to conduct risk analysis. The proposed framework was able to address several limitations attributed to the conventional application of failure mode and effect analysis and offered a generic framework that can be adapted to fit any industry or organization. / Construction Engineering and Management

Continuous-time Markov chains (CTMC's) form a convenient mathematical framework for analyzing random systems across many different disciplines. A specific research problem that is often of interest is to try to predict maximum probability sequences of state transitions given initial or boundary conditions. This work shows how to solve this problem exactly through an efficient dynamic programming algorithm. We demonstrate our approach through two different applications - ranking mutational pathways of HIV virus based on their probabilities, and determining the most probable failure sequences in complex fault-tolerant engineering systems. Even though CTMC's have been used extensively to realistically model many types of complex processes, it is often a standard practice to eventually simplify the model in order to perform the state evolution analysis. As we show here, simplifying approaches can lead to inaccurate and often misleading solutions. Therefore we expect our algorithm to find a wide range of applications across different domains.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第17885号 / 工博第3794号 / 新制||工||1580(附属図書館) / 30705 / 京都大学大学院工学研究科航空宇宙工学専攻 / (主査)教授 藤本 健治, 教授 泉田 啓, 教授 椹木 哲夫 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Yes / Flaring is a combustion process commonly used in the oil and gas industry to dispose flammable waste gases. Flare flameout occurs when these gases escape unburnt from the flare tip causing the discharge of flammable and/or toxic vapor clouds. The toxic gases released during this process have the potential to initiate safety hazards and cause serious harm to the ecosystem and human health. Flare flameout could be caused by environmental conditions, equipment failure, and human error. However, to better understand the causes of flare flameout, a rigorous analysis of the behavior of flare systems under failure conditions is required. In this article, we used fault tree analysis (FTA) and the dynamic Bayesian network (DBN) to assess the reliability of flare systems. In this study, we analyzed 40 different combinations of basic events that can cause flare flameout to determine the event with the highest impact on system failure. In the quantitative analysis, we use both constant and time-dependent failure rates of system components. The results show that combining these two approaches allows for robust probabilistic reasoning on flare system reliability, which can help improving the safety and asset integrity of process facilities. The proposed DBN model constitutes a significant step to improve the safety and reliability of flare systems in the oil and gas industry.

Real-world processes often undergo improvements to meet certain goals, such as coping with changed requirements, eliminating defects, improving the quality of the products, and reducing costs. Identifying and evaluating the defects or errors in the process, identifying the causes of such defects, and validating proposed improvements all require careful analysis of the process.Human-intensive processes, where human contributions require considerable domain expertise and have a significant impact on the success or failure of the overall mission, are of particular concern because they can be extremely complex and may be used in critical, including life-critical, situations. To date, the analysis support for such processes is very limited. If done at all, it is usually performed manually and can be extremely time-consuming, costly and error-prone.There has been considerable success lately in using static analysis techniques to analyze hardware systems, software systems, and manufacturing processes. This thesis explores how such analysis techniques can be automated and employed to effectively analyze life-critical, human-intensive processes. In this thesis, we investigated two static analysis techniques: Finite-State Verification (FSV) and Fault Tree Analysis (FTA). We proposed a process analysis framework that is capable of performing both FSV and FTA on rigorously defined processes. Although evaluated for processes specified in the Little-JIL process definition language, this is a general framework independent of the process definition language. For FSV, we developed a translation-based approach that is able to take advantage of existing FSV tools. The process definition and property to be evaluated are translated into the input model and property representation accepted by the selected FSV tool. Then the FSV tool is executed to verify the model against the property representation. For FTA, we developed a template-based approach to automatically derive fault trees from the process definition. In addition to showing the feasibility of applying these two techniques to processes, much effort has been put on improving the scalability and the usability of the framework so that it can be easily used to analyze complex real-world processes. To scale the analysis, we investigated several optimizations that are able to dramatically reduce the translated models for FSV tools and speed up the verification. We also developed several optimizations for the fault tree derivation to make the generated fault tree much more compact and easier to understand and analyze. To improve the usability, we provided several approaches that make analysis results easier to understand. We evaluated this framework based on the Little-JIL process definition language and employed it to analyze two real-world, human-intensive processes: an in-patient blood transfusion process and a chemotherapy process. The results show that the framework can be used effectively to detect defects in such real-world, human-intensive processes.

Yes / Internet of Things (IoT)-based smart agriculture systems are increasingly being used to improve agricultural yield. IoT devices used for agricultural monitoring are often deployed in outdoor environments in remote areas. Due to the exposure to harsh environments and the nature of deployment, sensors and other devices are susceptible to an increased rate of failure, which can take a system to unsafe and dangerous states. Failure of a smart agriculture system can cause significant harm to nature and people and reduce agricultural production. To address the concerns associated with the failure of the system, it is necessary to understand how the failures of the components of a system can contribute to causing the overall system failure. This paper adopts Fault Tree Analysis, a widely used framework for failure behaviour analysis in other safety-critical domains, to demonstrate the qualitative failure analysis of smart irrigation systems based on the components’ failure.

A method able to analyze and optimize subsystems could be useful to reduce project cost, increase subsystem reliability, improve overall aircraft safety and reduce subsystem weight. The earlier the optimization of development of an aircraft in the design phase can be performed, the better the yield of the optimization becomes. This master thesis was formed in order to construct an automatic analysis method, implementing a Matlab script, evaluating devices forming aircraft subsystems using a Genetic Algorithm. In addition to aircraft subsystems, the method constructed in the work is compatible with systems of various industries with minor modifications of the script.

With increasing competitive pressures in today‟s market, it has become critical for businesses to recognise the significance of satisfying their customers so as to ensure their economic stability. Various studies have emphasised on the need for customer focus and project satisfaction in the construction industry sector. The industry, however, has not fully embraced the practice of project satisfaction, which is grounded on meeting the needs of the customer. Though most research on project satisfaction has focussed on the client, it is essential that the satisfaction of the project delivery team and in the wider context, the stakeholders be considered. In this case, the client is the centre of gravity of the project team. In order to satisfy the project team, there are challenges in assessing their requirements. This necessitates the need to develop a unique and robust method for capturing and analysing the level of integrated project team satisfaction. In this research, the project delivery team and the stakeholders have been lumped together as an integrated project team. Therefore, integrated project team satisfaction entails recognising the client and project participants‟ requirements that guarantees project successful completion and acceptance by the team. In view of this, this research presents a framework, which has been developed to plug these needs and challenges. The framework, known as the Satisfaction Assessment Integrated Framework (SAIF) involves an integrated approach that considers the participants of a construction project as a tree structure, and each member of that tree as an intermediate or top element. Relationships and interactions of the elements, and how these affect the overall satisfaction levels of a single project, are analysed based on understanding their requirements and invoking modern satisfaction attainment theory. The framework includes a method for understanding and identifying the satisfaction attributes; multi-attribute analysis for prioritising the satisfaction attributes of the clients and project participants; fault tree analysis strategy for defining the satisfaction relationship in a particular project team; and an assessment scoring system (a combination of multi-attribute analysis, and failure mode and effects analysis methodical approach) that evaluates how much each member of the project team meets the requirements or satisfaction attributes of other participants. Hence, SAIF, a novel assessment methodology, investigates and identifies possible links and the influence of integrating the construction project team and their satisfaction attributes with the aim of improving their satisfaction levels as a team. Through the findings of this research, recommendations are made to further explore the implications of satisfying a given participant against dissatisfying the participant; and subsequently improve the satisfaction assessment process.

根據研究報告調查發現，即使組織已經使用了安全機制仍無法完全阻止危害組織資訊安全事件的發生，這是因為組織的資訊安全管理是一個不斷改善的過程，並不是使用了安全防護措施之後，就可以高枕無憂，除了架構安全防護機制外，還需要去分析資訊的機密性、完整性或可得性等是否真能夠受到保護？所使用的安全機制是否真能解決組織的資訊安全問題？或是所提供的安全程度是否能接受等？ 為了解決上述等問題，本研究希望從管理的角度切入，應用錯誤樹分析方法在資訊安全管理的領域上，希望藉由此方法幫助管理者獲知組織的資訊安全需求，然後透過資訊安全管理不斷改善的過程，改善組織資訊安全的弱點，提高組織安全的可靠度。 依據研究架構，結合BS7799此資訊安全管理標準，並應用錯誤樹分析方法，將資訊安全政策轉換為資訊安全模型，由此資訊安全模型作進一步的定性與定量分析；本研究利用錯誤樹分析方法的六個步驟，實際模擬組織資訊安全需求獲得的過程，並透過分析的結果，幫助組織從中獲取資訊安全的需求，找出資訊安全的弱點，作為組織資訊安全改進的參考與依據。 / As the investigate report dictated, the degree of security of an information system does not only depend on the security mechanism installed by the organization. It is a continuous and recursive procedure. Most researches are technique-oriented currently. In order to adjust this bias, this research propose a new approach, which is from the management perspective. BS7799 is used for the information security policy reference. FTA is used to build up the information security model and acquire the requirements of an information system and verify its effectiveness. The result can promote the reliability of the information system and reduce the vulnerability of the system too.