An Approach for Reliability Demonstration Test Based on Power‐Law Growth Model

Xu, Junjie; Yu, Dan; Xie, Min; Hu, Qingpei

doi:10.1002/qre.2138

Cited by 13 publications

(4 citation statements)

References 38 publications

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“…Chen et al [14] consider a multi-stage system with NHPP at each stage and models growth factors. Non-Bayesian approaches to modelling RDGT using power law growth models such as NHPP are still areas of active research--see, e.g., Xu et al [15].…”

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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Section: Literaturementioning

confidence: 99%

Bayesian Modelling for Product Testing and Release

Wilson¹

2020

IFIP Advances in Information and Communication Technology

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“…It is one of the effective technologies to improve the reliability of products. [1][2][3][4][5][6] However, with the development of science and technology, many repairable systems such as power devices, [7] control instruments, [8] aerospace components, [9] etc., which have high reliability and their failure intervals are quite long. RGT under the normal stress requires a long test cycle and consumes a lot of cost.…”

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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“…The TBF of NC machine tool is non‐independent and identically distributed. The model to evaluate the reliability of NC machine tool mainly includes Poisson process, non‐homogeneous Poisson process, trend renewal process, and power law distribution . Among them, Rigdon proved that the PLM can better evaluate the reliability of complex repairable systems based on empirical statistical inference methods .…”

Section: Introductionmentioning

confidence: 99%

Application of power law model in reliability evaluation of machine tools by considering working condition difference

Zhu

et al. 2018

Quality & Reliability Eng

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The influence of working condition difference has not been considered in the traditional reliability modeling of numerical control (NC) machine tools. To solve the problem, a reliability evaluation method based on mixture variable parameter power law model (MVPPLM) is proposed in this study. First, the scale parameter of the PLM is obtained by multi‐dimensional exponential distribution. Second, a proportional relation of failure rate function between each working condition and reference working condition is established. The proportion coefficient is solved using the partial likelihood function. Working condition factors with a significant influence on reliability levels are selected through the chi‐squared test. Third, reliability evaluation models under different working condition levels are established through mixture distributions. The mixture weight coefficient is calculated by the standard deviation of working condition covariates. The maximum likelihood estimation method is used to estimate parameters. Finally, results of a case analysis based on the data of NC machine tools in the user field tracing test show that the MVPPLM has higher precision than the traditional method. Therefore, reliability evaluation that considers working condition difference is valuable for engineering application.

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An Approach for Reliability Demonstration Test Based on Power‐Law Growth Model

Cited by 13 publications

References 38 publications

Bayesian Modelling for Product Testing and Release

Bayesian Modelling for Product Testing and Release

A highly accelerated stress reliability growth test method

Application of power law model in reliability evaluation of machine tools by considering working condition difference

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