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A reliability analysis and evaluation model based on event chain and Bayesian information fusion is proposed for the characteristics of complex space phased‐mission systems (SPMSs), the implementation process of integrated reliability modeling is detailed, and the identification of weak links and reliability evaluation is carried out with the entry, descent, and landing process of Tianwen‐1 Mars Probe as an example. The application results show that the reliability analysis and evaluation model based on event chain and Bayesian information fusion has good engineering applicability, and the method can be extended and applied to the reliability modeling, analysis, and evaluation of complex SPMSs. [ABSTRACT FROM AUTHOR]

Reliability target definition is a crucial aspect of any reliability analysis. In literature, there are two types of analysis. The first one, called 'bottom-up', goes back to the system's target using data of units through a fault tree analysis. Reliability data of components could be only partially available, particularly in the case of innovative systems. In the second type of analysis, called 'top-down', starting from similar systems, the target of each unit is defined, by applying allocation techniques. Also, in this case, reliability data of similar systems might not be available, and the choice of the most appropriate technique could be tricky. The purpose of the present research is to combine the advantages of both usual approaches. The newly developed approach is based on the integrated factors method, whose values are adjusted trough a multicriteria method, the analytic hierarchy process, depending on the importance of each factor and each unit. The innovation of the proposed model consists in its dynamism, as most of the literature methods use constant weights for the factors involved in reliability allocation. No method takes into account the assignment of a different level of significance (weight) to different units of the system, simultaneously with the considered factors. The developed approach has been applied on an aerospace prototype system. The results show the goodness of the new method and its ability to overcome the problems noted in literature. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic logic-based method for quantitative risk assessment using fault tree analysis (FTA) that can take into account both types of uncertainties including objective and subjective uncertainties. In the proposed method, each fault tree gate is translated to its corresponding stochastic logic template and then is implemented on a field programmable gate array (FPGA). Because the analysis does not utilize any transformation methods, the results of analysis are more accurate than those methods which are based on transformation from possibility to probability distributions or vice versa. Experimental results for a benchmark fault tree show that this method accelerates analysis time compared to conventional hybrid uncertainty analysis method and transformation methods. The efficiency of the proposed method is demonstrated by implementation in a real steel structure project. The quantitative risk assessment is performed for the incomplete penetration as one of the defects encountered in arc welding process, and its results are compared with transformation methods. The comparison results show the proposed hybrid uncertainty analysis method is also more accurate in comparison to the transformation-based approaches. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Fault tree analysis is a method largely used in probabilistic risk assessment. Uncertainties should be properly handled in fault tree analyses to support a robust decision making. While many sources of uncertainties are considered, dependence uncertainties are not much explored. Such uncertainties can be labeled as 'epistemic' because of the way dependence is modeled. In practice, despite probability theory, alternative mathematical structures, including possibility theory and fuzzy set theory, for the representation of epistemic uncertainty can be used. In this article, a fuzzy β factor is considered to represent the failure dependence uncertainties among basic events. The relationship between β factor and system failure probability is analyzed to support the use of a hybrid probabilistic-possibilistic approach. As a result, a complete hybrid probabilistic-possibilistic framework is constructed. A case study of a high integrity pressure protection system is discussed. The results show that the proposed method provides decision makers a more accurate understanding of the system under analysis when failure dependencies are involved. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper provides a way to deal with issues related to design for reliability using axiomatic design (AD). A theoretical approach is studied, starting from the traditional theory of AD, in order to help designers to optimize the product's reliability using a structured approach. The aim of this work is to introduce a new method that is able to assess the product reliability using the support of the AD methodology combined with other methods, e.g. FMEA and FTA. The approach developed is called failure mode and effect tree analysis (FMETA). FMETA allows the designer to find the most critical characteristic of the product from a reliability point of view and provides the designer with a set of possible changes. The core of this work is the development of a reliability tree, used to evaluate both the RPN for the component of the product and to find the reliability relation useful for the following optimization. The reliability tree is born from the combined use of AD, FMEA and FTA. This method has been validated by an application to an automotive heavy-duty diesel engine. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]