A Reliability Growth Projection Model for One-Shot Systems

Hall, JW; Mosleh, Ali

doi:10.1109/tr.2007.909774

Cited by 15 publications

(7 citation statements)

References 12 publications

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“…The result in (20) is then updated as (37) For the prior number of failure modes used in the visual goodness of fit in Section IV, the likelihood of observing a failure mode on at least one of the systems by time is updated as (38) The expression in (28) is then modified to be (39) When comparing the observed failure modes to the prior prediction, note that the time of first occurrence for a failure mode should be the minimum of the occurrence times observed over all of the systems under test. Modifications to the chi-square test in Section IV.B are not necessary for multiple systems, as (33) and (34) can be used directly in place of (9) and (10).…”

Section: A Multiple Systems Under Testmentioning

confidence: 98%

“…Crow also combined techniques from [12] and [16] in the popular Crow-Extended Model [19] to allow for arbitrary corrective actions. More recent work by Hall [20], [21] has dealt with reliability growth projection for discrete systems, developed in part due to the lack of modeling techniques for these types of systems. Due to the large number of potential software reliability growth models that can be used, Li, et al [22] employed adaptive boosting techniques to combine results across multiple software reliability growth models.…”

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confidence: 99%

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A Bayesian Model for Complex System Reliability Growth Under Arbitrary Corrective Actions

Wayne

Modarres²

2015

IEEE Trans. Rel.

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This paper presents a new method for projecting the reliability growth of a complex continuously operating system. The model allows for arbitrary corrective action strategies, and it differs from other models of this type by using all available data rather than failure mode first occurrence times only. It also differs from other reliability growth projection models in that it provides a complete inference framework via the posterior distribution on the system failure intensity. A unique feature of this approach relative to other Bayesian techniques is the analytic expression for the failure intensity contribution from unobserved failure modes. Expressions for estimating the initial failure intensity, growth potential failure intensity, and the cumulative number of failure modes expected in future testing are also developed. Extensions to the basic framework are also developed. The first accounts for multiple systems under test, and the second develops the posterior distribution while allowing for uncertainty on the Fix Effectiveness Factor values that are assessed. Two separate goodness-of-fit procedures are presented for assessing the appropriateness of the underlying model assumptions.Index Terms-Bayesian reliability, fix effectiveness, reliability growth, reliability growth projection.

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Section: A Multiple Systems Under Testmentioning

confidence: 98%

mentioning

confidence: 99%

A Bayesian Model for Complex System Reliability Growth Under Arbitrary Corrective Actions

Wayne

Modarres²

2015

IEEE Trans. Rel.

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“…In a recent study, Hall and Mosleh developed a mathematical formulation of discrete reliability growth method, which models system reliability improvement due to changes in system design after failure modes are identified during tests. The approach is based on failure on demand and its applicability to modeling software failure remains to be evaluated.…”

Section: A Systematic Reviewmentioning

confidence: 99%

Suitability analysis of software reliability models for its applicability on NPP systems

Kumar

Singh

Kumar

2018

Quality & Reliability Eng

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The advent of software components in the safety critical systems of nuclear power plant has introduced new challenges for software professionals to provide increased software reliability. Several proposals have been proposed for ensuring software reliability in different phases of software development life cycle. The present article is a novel attempt in providing an exhaustive survey of software reliability models for their applicability on safety critical systems of nuclear power plants. Our systematic review shows that none of the proposals for ensuring software reliability are applicable on such systems. The issues and challenges are discussed, and a software reliability model for safety critical system of nuclear power plants is proposed.

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“…In the example below, uncertainty distributions are constructed for all of the management metrics given in [18]. Note that the associated equations are derived from a shrinkage factor estimator discussed in [17], and [2]. The management metrics and associated model equations include: the expected initial reliability of the system (obtained from Equation (8) in [18]),…”

Section: Monte Carlo Simulationmentioning

confidence: 99%