Importance measures for non-coherent-system analysis

Component importance analysis is a key part of the system reliability quantification process. It enables the weakest areas of a system to be identified and indicates modifications, which will improve the system reliability. Although a wide range of importance measures have been developed, the majority of these measures are strictly for coherent system analysis. Non-coherent systems can occur and accurate importance analysis is essential. This paper extends four commonly used measures of importance, using the noncoherent extension of Birnbaum’s measure of component reliability importance. Since both component failure and repair can contribute to system failure in a noncoherent system, both of these influences need to be considered. This paper highlights that it is crucial to choose appropriate measures to analyze component importance. First the aims of the analysis must be outlined and then the roles that component failures and repairs can play in system state deterioration can be considered. For example, the failure/repair of components in safety systems can play only a passive role in system failure, since it is usually inactive, hence measures that consider initiator importance are not appropriate to analyze the importance of these components. Measures of importance must be chosen carefully to ensure analysis is meaningful and useful conclusions can be drawn

Calculating the failure intensity of a non-coherent fault tree using the BDD technique.

This paper considers a technique for calculating the unconditional failure intensity of any given non-coherent fault tree. Conventional Fault Tree Analysis (FTA) techniques involve the evaluation of lengthy series expansions and approximations are unavoidable even for moderate sized fault trees. The Binary Decision Diagram (BDD) technique overcomes some of the shortfalls of conventional FTA techniques enabling efficient and exact quantitative analysis of both coherent and non-coherent fault trees

Birnbaum’s measure of component importance for noncoherent systems

Importance analysis of noncoherent systems is limited, and is generally inaccurate because all measures of importance that have been developed are strictly for coherent analysis. This paper considers the probabilistic measure of component importance developed by Birnbaum (1969). An extension of this measure is proposed which enables noncoherent importance analysis. As a result of the proposed extension the average number of system failures in a given interval for noncoherent systems can be calculated more efficiently. Furthermore, because Birnbaum’s measure of component importance is central to many other measures of importance; its extension should make the derivation of other measures possible

Non-coherent fault tree analysis

The aim of this thesis is to extend the current techniques available for the analysis of non-coherent fault trees. At present importance analysis of non-coherent systems is extremely limited. The majority of measures of importance that have been developed can only be used to analyse coherent fault trees. If these measures are used to analyse non-coherent fault trees the results obtained are inaccurate and misleading. Extensions for seven of the most commonly used measures of importance have been proposed to enable accurate analysis of non-coherent systems. The Binary Decision Diagram technique has been shown to provide an accurate and efficient means of analysing coherent fault trees. The application of this technique for the qualitative analysis of non-coherent fault trees has demonstrated the gains to be made in terms of efficiency and accuracy. Procedures for quantifying a non-coherent fault tree using this technique have been developed; these techniques enable significantly more efficient and accurate analysis than the conventional techniques for Fault Tree Analysis. Although the Binary Decision Diagram technique provides an efficient and accurate means of analysing coherent and non-coherent fault trees, large trees with many repeated events cannot always be analysed exactly. In such circumstances partial analysis must be performed if any conclusions regarding system safety and reliability are to be drawn. Culling techniques employed in conjuncfion with the Binary Decision Diagram method have been developed for the partial analysis of both coherent and non-coherent fault trees

Calculating the failure intensity of a non-coherent fault tree using the BDD technique

Calculating the failure intensity of a non-coherent fault tree using the BDD techniqu

Quantitative analysis of a non-coherent fault tree structure using binary decision diagrams

Quantitative analysis of a non-coherent fault tree structure using binary decision diagram