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            ayesian Reliability Demonstration

Coolen, Frank P. A.; Coolen‐Schrijner, Pauline

doi:10.1002/9780470061572.eqr078

Cited by 6 publications

(6 citation statements)

References 21 publications

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“…This is briefly illustrated in Example 3 in section 4, and discussed in more detail in section 6 for more complex systems. The results in this paper can also be used in a straightforward manner to take into account the costs of components and of testing, together with practical constraints on test budget and time, in line with the Bayesian approach to such problems [29,30]. This is also briefly discussed in section 6, but it is mostly left as an interesting and important topic for future research.…”

Section: Npi For K-out-of-m Systemsmentioning

confidence: 95%

“…For example, components considered could be batteries required to provide back-up in case of problems with electricity supply for a safety critical system, where system functioning requires a minimum of three batteries to function when demanded, but where installing more batteries might provide important redundancy. Rahrouh et al [29] presented a Bayesian approach for optimal decisions for reliability demonstration (see also reference [30]), assuming that only component tests with zero failures would lead to release of the system for practical use, as is often the case if high reliability is required. They considered both the costs of testing and the costs of extra system redundancy, and also took into account practical constraints with regard to test time and budget.…”

Section: Examplementioning

confidence: 99%

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Nonparametric predictive inference for system reliability with redundancy allocation

Coolen‐Schrijner

Coolen

MacPhee

2008

Proceedings of the Institution of Mechanical Engineers, Part O:

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This paper presents lower and upper probabilities for the reliability of k-out-of-m systems, which include series and parallel systems, and of series systems with independent k i -out-of-m i subsystems, for which optimal redundancy allocation is also presented in case of zero-failure testing. First, attention is restricted to k-out-of-m systems with exchangeable components. The lower and upper probabilities for successful functioning of the system are based on the nonparametric predictive inferential (NPI) approach for Bernoulli data. In this approach, it is assumed that test data are available on the components, and that the future components to be used in the system are exchangeable with these. Thereafter, systems are considered that consist of a series of independent subsystems, with subsystem i a k i -out-of-m i system consisting of exchangeable components. For such systems, an algorithm for optimal redundancy allocation after zero-failure testing is presented. A particularly attractive feature of NPI in reliability, with lower and upper probabilities, is that data containing zero failures can be dealt with in an attractive manner.

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Section: Npi For K-out-of-m Systemsmentioning

confidence: 95%

Section: Examplementioning

confidence: 99%

Nonparametric predictive inference for system reliability with redundancy allocation

Coolen‐Schrijner

Coolen

MacPhee

2008

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…Many researchers have used the Bayesian reliability demonstration theorem [25][26][27] to come up with more reliable initial assumptions for the model parameters. The Bayesian methods considered various ''prior information'' such as history test data of similar products.…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of test times for reliability growth testing with interval-valued model parameters

Wang

Hou

2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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Reliability growth testing is widely used to identify and remove failure modes in the development of complex systems. Different models have been proposed to track the progress of reliability growth during test, and previous research has addressed the improvement of after-testing system reliability by allocating limited testing resources. The majority of reliability growth testing models are based on the AMSAA/Crow model with known parameters, but there is a lack of work focusing on the situation when the AMSAA/Crow parameters are subject to uncertainty. In this article, we investigate a reliability growth testing allocation problem to series–parallel systems that considers parameter uncertainty in the AMSAA/Crow models. The model parameters are assumed to be known as uncertain-but-bounded values. Interval arithmetic and an interval order relation reflecting decision maker’s preference are used to analyze the uncertain parameters. In order to determine the optimal allocation of testing time for each component or subsystem aiming to maximize the after-testing system reliability, a modified genetic algorithm is developed. A penalty function is used to handle the testing resource limitations, and the techniques of dual mutation and random keys are used in the algorithm to improve searching efficiency. Computational results indicate that the interval analysis is a useful tool to deal with parameter uncertainty of reliability growth testing allocation problems.

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“…Moreover, the 2008 Defense Science Board Task Force raised a major concern regarding the major risks of the initial assumptions [9]. As a result, many researches use the Bayesian reliability demonstration theorem [10] [11] [12] to come up with more reliable initial assumptions. The Bayesian method considers various prior information such as historical test data, field data, design changes, corrective actions, and field data for similar products and factors this information in the initial assumptions.…”

Section: Introductionmentioning

confidence: 99%

Economic allocation of reliability growth testing using Weibull distributions

Awad

2016

Reliability Engineering & System Safety

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B ayesian Reliability Demonstration

Abstract: This paper presents several main aspects of Bayesian reliability demonstration, together with a concise discussion of key contributions to this topic.

Cited by 6 publications

References 21 publications

Nonparametric predictive inference for system reliability with redundancy allocation

Nonparametric predictive inference for system reliability with redundancy allocation

Optimal allocation of test times for reliability growth testing with interval-valued model parameters

Economic allocation of reliability growth testing using Weibull distributions

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