Data analysis and resource allocation in Bayesian selective accelerated reliability growth

Ruiz, César; Heydari, Mohammadhossein; Sullivan, Kelly M.; Liao, Haitao; Pohl, Edward A.

doi:10.1080/24725854.2019.1567957

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…Ruiz et al 6 considered a complex system with multiple components each having multiple failure modes, and applied a simplified version of this model for reliability growth planning. 7 The increased availability of degradation data and improvement of computing capabilities has increased the interest on degradation-based reliability modeling in the last two decades. Failure time due to degradation is typically defined as first time that a degradation signal crosses a user-defined threshold.…”

Section: F I G U R E 1 Example Of Degradation Signals With Different Failure Thresholdsmentioning

confidence: 99%

Analysis of correlated multivariate degradation data in accelerated reliability growth

Ruiz

Liao

Pohl

2020

Quality & Reliability Eng

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Modern engineering systems have become increasingly complex and at the same time are expected to be developed faster. To shorten the product development time, organizations commonly conduct accelerated testing on a small number of units to help identify failure modes and assess reliability. Many times design changes are made to mitigate or reduce the likelihood of such failure modes.Since failure-time data are often scarce in reliability growth programs, existing statistical approaches used for predicting the reliability of a system about to enter the field are faced with significant challenges. In this work, a statistical model is proposed to utilize degradation data for system reliability prediction in an accelerated reliability growth program. The model allows the components in the system to have multiple failure modes, each associated with a monotone stochastic degradation process. To take into account unit-to-unit variation, the random effects of degradation parameters are explicitly modeled. Moreover, a mean-degradation-stress relationship is introduced to quantify the effects of different accelerating variables on the degradation processes, and a copula function is utilized to model the dependency among different degradation processes. Both a maximum likelihood (ML) procedure and a Bayesian alternative are developed for parameter estimation in a two-stage process. A numerical study illustrates the use of the proposed model and identifies the cases where the Bayesian method is preferred and where it is better to use the ML alternative.

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Section: F I G U R E 1 Example Of Degradation Signals With Different Failure Thresholdsmentioning

confidence: 99%

Analysis of correlated multivariate degradation data in accelerated reliability growth

Ruiz

Liao

Pohl

2020

Quality & Reliability Eng

Self Cite

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“…Bayesian methods for estimating the reliability of systems are examined in Li et al [16] and Ruiz et al [17]. A comparison of classical versus Bayesian methods can be found in Kamranfar et al [18].…”

Section: Literaturementioning

confidence: 99%

Bayesian Modelling for Product Testing and Release

Wilson¹

2020

IFIP Advances in Information and Communication Technology

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Deciding when to release a new product requires a tradeoff between costs, potential profits and the underlying reliability of a product. Many new products go through a "Test Analyze and Fix" process. When a failure occurs, an immediate design "fix" may occur or the product might undergo a minimal fix with design changes being delayed until later when many changes can be introduced at the same time. We introduce a Bayesian model that allows for the introduction of managerial knowledge and experience. Unlike most approaches, we do not build in an assumption that the product always improves throughout the process.

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“…In Ruiz et al., [ 22 ] a statistical model was proposed to utilize degradation data for system reliability prediction in an accelerated reliability growth program. In Ruiz et al., [ 23 ] a Bayesian selective accelerated reliability growth method was proposed to accelerate the potential failure modes.…”

Section: Introductionmentioning

confidence: 99%

A highly accelerated stress reliability growth test method

Xing

Yang

2022

Quality & Reliability Eng

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Reliability growth test (RGT) is one of the main methods to improve product reliability. In order to effectively solve the problem of short development cycles faced by enterprises, the accelerated reliability growth test (ARGT) is developed. Since the traditional ARGT is carried out separately under all of the accelerated stress levels, and the time of RGT is too long under low accelerated stress levels. So, a highly accelerated stress reliability growth test (HARGT) is proposed, where the RGT is designed to carry out at the highest accelerated stress level. Firstly, AMSAA model is adopted to track the reliability growth process under the highest accelerated stress level, and its parameters are determined by the maximum likelihood estimation method. Secondly, taking temperature as the accelerated stress, the relationship of the reliability with different accelerated stress levels is established under the accelerated life test (ALT) in the first stage. Based on the Arrhenius model and combined with the acceleration coefficient, the product reliability at the end of the test is extrapolated to the product reliability at the normal stress level. The parameters of the Arrhenius model are got through the least square estimation method, and the acceleration coefficient and equation are also obtained. Finally, a case study on the communication service system is given to show the effectiveness of the proposed method.

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Data analysis and resource allocation in Bayesian selective accelerated reliability growth

Cited by 6 publications

References 32 publications

Analysis of correlated multivariate degradation data in accelerated reliability growth

Analysis of correlated multivariate degradation data in accelerated reliability growth

Bayesian Modelling for Product Testing and Release

A highly accelerated stress reliability growth test method

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