An integrated approach to redundancy allocation and test planning for reliability growth

Heydari, Mohammadhossein; Sullivan, Kelly M.

doi:10.1016/j.cor.2017.12.013

Cited by 19 publications

(8 citation statements)

References 38 publications

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“…The components used in each step often work in essentially different operating modes, characterized by varying loads and performance, demand management of a different nature, or different environmental conditions [4]. These modes result in different failure rates and life distributions.…”

Section: Introductionmentioning

confidence: 99%

Some Efficient Algorithms to Deal With Redundancy Allocation Problems

Es-Sadqi¹,

Idrissi²,

Benhassine³

2021

JAMRIS

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Section: Introductionmentioning

confidence: 99%

Some Efficient Algorithms to Deal With Redundancy Allocation Problems

Es-Sadqi¹,

Idrissi²,

Benhassine³

2021

JAMRIS

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“…Heydari and Sullivan 31 studied a robust allocation problem of testing resources in reliability growth for both series and series–parallel systems. Heydari and Sullivan 37 developed a new model that merged testing components and installing redundancies within an integrated optimization in reliability growth.…”

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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“…So far, more attention has been paid to test resources allocation with the objective of product reliability maximization [7,8,9,10]. Distinguishing the existing literature, in this paper, considering cost minimization, we deal with a reliability growth test program for repairable products subject to specified reliability growth target and sold with a two-dimensional warranty policy.…”

Section: Introductionmentioning

confidence: 99%

Optimal Reliability Growth Program for Repairable and Warranted Products

Wang¹,

Liu

2019

2019 Annual Reliability and Maintainability Symposium (RAMS)

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Considering a two-dimensional and non-renewing free repair warranty (NFRW) policy, this paper proposes a modified reliability growth model to support the decision making of the manufacturer on the choice of a reliability growth test program for repairable products with a twofold Weibull lifetime distribution, which has a bathtub-shaped failure rate function. Excess usage is used as a stress to trigger failures and the effect of usage acceleration on product reliability is modeled by the Accelerated Failure Time (AFT) approach. Learning from test failures is incorporated into periodic fixes through which the overall product failure intensity is reduced through discrete steps. The optimal test duration and the optimal number of fixes are both obtained such that the expected total cost per product to the manufacturer is minimized while achieving the stated reliability growth target at the same time. Results of the illustrative case show that the optimal reliability growth test setting is governed by a sensitive tradeoff between the test cost incurred and the reduction in the warranty cost. A sensitivity analysis on several key input parameters of the proposed model reveals the importance of conducting reliability growth, especially for products with heavy usage intensity and high repair cost during field use. It is also found with a higher failure learning level, the optimal reliability growth test program yields more prominent cost reduction and reliability improvement.

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An integrated approach to redundancy allocation and test planning for reliability growth

Cited by 19 publications

References 38 publications

Some Efficient Algorithms to Deal With Redundancy Allocation Problems

Some Efficient Algorithms to Deal With Redundancy Allocation Problems

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

Optimal Reliability Growth Program for Repairable and Warranted Products

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