Robust allocation of testing resources in reliability growth

Heydari, Mohammadhossein; Sullivan, Kelly M.

doi:10.1016/j.ress.2017.11.026

Cited by 11 publications

(20 citation statements)

References 29 publications

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“…For the series–parallel system with identical components in each subsystem, the components in a parallel subsystem are homogeneous. Following Heydari and Sullivan, 31 we assumed that the N subsystems are collectively independent and no connection and integration failures occur. The system reliability improvement is related to subsystem test times τ i , where i = 1 , 2 ,… , N .…”

Section: Model Descriptionmentioning

confidence: 99%

“…29 To tackle the drawback of initial assumptions, Coit 20 and Awad 30 have concerned the limitation of uncertainty in their models of allocating testing times by considering the variability of failure intensity rate estimate. Heydari and Sullivan 31 studied the robust allocation problem of testing resources in reliability growth; the AMSAA/Crow parameters in their model are uncertain but assumed to lie within a budget-restricted uncertainty set. With exceptions of the aforementioned studies, the literature rarely considered the potential uncertainties of reliability growth model parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Levitin 33 generalized a reliability growth test allocation problem to series–parallel multi-state systems. Heydari et al 34 considered the connection-induced failures and modeled the optimal allocation of component-, subsystem- and system-level testing resources in the reliability growth of a series–parallel system. Awad 30 proposed a new method to allocate RGT time for both the subsystem and system levels in order to minimize system failure intensity under limited cost and time resources.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al 36 proposed a multi-objective and multi-stage reliability growth planning approach with the goal of providing accurate and realistic reliability prediction in the early product development stage, and they analyzed the trade-off analysis among multiple product development objectives including development cost, testing time and projected reliability. 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%

“…For the series–parallel systems, Heydari et al 34 and Heydari and Sullivan, 31 which have been mentioned above, proposed two different reliability growth models that described the relationship between the improvement in a system/subsystem/component design. In Heydari et al, 34 three levels of RGT according to the AMSAA/Crow model are conducted for a series–parallel system, which are system-, subsystem- and component-level testing. Each level has both advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Vehicle-cloud-collaborated prognosis and health management for lithium-ion batteries: Framework, technics and perspective

Sida,

Zichao,

Honglei

et al. 2024

Energy Storage Materials

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Multi-phase reliability growth test planning for repairable products sold with a two-dimensional warranty

Wang

Liu

et al. 2019

Reliability Engineering & System Safety

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For products sold with a two-dimensional warranty policy, the warranty servicing cost can be reduced through reliability growth during development. This paper investigates a multi-phase reliability growth test program for repairable products with independent competing failure modes.Considering a test-find-test scheme, an accelerated reliability growth model is developed, allowing different failure modes having distinct accelerated relationships. Taking usage heterogeneity and mode-specific failure learning effect into account, the reliability growth is achieved through periodic fixes on a phase-by-phase basis. From the manufacturer's perspective, the main objective is to achieve the optimal trade-off between the warranty cost and the reliability growth test cost by determining the optimal test program that minimizes the expected total cost per product sold, ensuring that the pre-specified reliability growth requirement is met. Assuming the tri-Weibull product failure distribution, we illustrate the proposed optimization model numerically and study the effect of relevant parameters on the optimal reliability growth test program. The results show that the proposed approach yields significant cost reduction and reliability improvement for the examples studied in this paper, especially when the manufacturer possesses high failure learning ability, and the product has expensive repair cost per a warranty failure and extensive warranty coverage.

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Robust allocation of testing resources in reliability growth

Cited by 11 publications

References 29 publications

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

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

Vehicle-cloud-collaborated prognosis and health management for lithium-ion batteries: Framework, technics and perspective

Multi-phase reliability growth test planning for repairable products sold with a two-dimensional warranty

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