Planning, tracking, and projecting reliability growth a Bayesian approach

Strunz, Richard; Herrmann, Jeffrey W.

doi:10.1109/rams.2012.6175512

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…To accommodate arbitrary corrective strategies, Crow developed an extended model that combines the Crow tracking model and the Crow projection mode [9]. To estimate the discrete system reliability based on planning methodology, a full Bayesian estimation was proposed by Strunz and Herrmann by considering all components, subsystems, and system-level test data [10]. Based on essential function failures (EFF), a novel method for planning system reliability growth was proposed by Bell and Bearden by taking into account the management strategy [11].…”

Section: Introductionmentioning

confidence: 99%

Research on the testing duration of reliability growth planning

Li,

Wei

2024

Preprint

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Reliability growth plans are typically described by using reliability growth planning models that generate growth planning curves. The PM2 model, also known as the planning model based on projection methodology, is a widely used growth planning model. The PM2 model can always meet the assigned reliability target during any reliability growth testing duration, regardless of changes in the management strategy parameter and/or the fixed effectiveness factor parameter. In fact, these two parameters determine whether reliability growth testing is successful or not and how long it takes. That is, the PM2 model currently in place needs to be modified accordingly. Firstly, a sensitivity analysis is carried out on the three parameters of the PM2 model, followed by a discussion on the correlation between the product of two process management parameters and the duration of testing. Then, a relationship equation for the testing duration is constructed using these two process management parameters. Finally, an extended model for planning reliability growth has been proposed. In addition, the proposed planning model is utilized to examine an illustrated example and a real case. The results have demonstrated that the model is reasonable and reliable, as the subjective input, testing duration, was removed from the reliability growth planning model.

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Section: Introductionmentioning

confidence: 99%

Research on the testing duration of reliability growth planning

Li,

Wei

2024

Preprint

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“…In this setting, it is possible to calculate the moments of system reliability using the moments of component reliability via block decomposition for Bayesian and traditional statistical approaches Thompson and Haynes provide a Beta distribution to approximate the system reliability distribution using the method of moments. Using ALT data Strunz and Herrmann develop a model for Bayesian reliability growth analysis for a one‐shot system such as a projectile weapon Ruiz et al develop a model to estimate the reliability of a complex system using block decomposition on an ALT‐based reliability growth program implemented for the components.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian framework for accelerated reliability growth testing with multiple sources of uncertainty

Ruiz

Pohl

Liao

et al. 2018

Quality & Reliability Eng

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Reliability growth tests are often used for achieving a target reliability for complex systems via multiple test‐fix stages with limited testing resources. Such tests can be sped up via accelerated life testing (ALT) where test units are exposed to harsher‐than‐normal conditions. In this paper, a Bayesian framework is proposed to analyze ALT data in reliability growth. In particular, a complex system with components that have multiple competing failure modes is considered, and the time to failure of each failure mode is assumed to follow a Weibull distribution. We also assume that the accelerated condition has a fixed time scaling effect on each of the failure modes. In addition, a corrective action with fixed ineffectiveness can be performed at the end of each stage to reduce the occurrence of each failure mode. Under the Bayesian framework, a general model is developed to handle uncertainty on all model parameters, and several special cases with some parameters being known are also studied. A simulation study is conducted to assess the performance of the proposed models in estimating the final reliability of the system and to study the effects of unbiased and biased prior knowledge on the system‐level reliability estimates.

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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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Planning, tracking, and projecting reliability growth a Bayesian approach

Cited by 4 publications

References 11 publications

Research on the testing duration of reliability growth planning

Research on the testing duration of reliability growth planning

A Bayesian framework for accelerated reliability growth testing with multiple sources of uncertainty

Analysis of correlated multivariate degradation data in accelerated reliability growth

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