Reliability Assessment for Very Few Failure Data and Zero-failure Data

Jia, Xiang

doi:10.3901/jme.2016.02.182

Cited by 15 publications

(13 citation statements)

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“…It is estimated that the probability of failure at each truncation time is the most important step in the matching distribution curve method. The traditional method of estimating failure probability is given by Jia et al, and its expression is

{\hat{normalp}}_{TEi} = 0.5 / ((), s_{i} + 1)

. Bayesian estimation method is a common method for estimating the failure probability p i , including classical Bayesian method, hierarchical Bayesian method, and E‐Bayesian method.…”

Section: Point Estimate Of Product Reliabilitymentioning

confidence: 99%

Section: Estimates Of Failure Probabilitymentioning

confidence: 99%

“…Then, the reliability of the product was estimated. The method distributes the lifetime probability distribution curve was widely adopted because better results can be obtained . Although the results of the reliability assessment can be recognized, only the point estimates of distribution parameters were given.…”

Section: Introductionmentioning

confidence: 99%

“…The method distributes the lifetime probability distribution curve was widely adopted because better results can be obtained. [20][21][22][23] Although the results of the reliability assessment can be recognized, only the point estimates of distribution parameters were given. The basic purpose of the bootstrap method is to expand the number of samples.…”

Section: Introductionmentioning

confidence: 99%

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Reliability assessment of high‐quality and long‐life products based on zero‐failure data

Xie

et al. 2018

Quality & Reliability Eng

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For a period of mission time, only zero‐failure data can be obtained for high‐quality long‐life products. In the case of zero‐failure data reliability assessment, the point estimates and confidence interval estimates of distribution parameters cannot be obtained simultaneously by the current reliability assessment models, and the credibility of the assessment results may be reduced if they are obtained at the same time. A new model is proposed for consistency problem in this paper. In the proposed model, the point estimates of reliability can be obtained by the lifetime probability distribution derived from matching distribution curve method, while the confidence interval estimates of reliability can be obtained by using new samples generated from the lifetime probability distribution according to parameter bootstrap method. By analyzing the zero‐failure data of the torque motors after real operation, the results show that the new model not only meets the requirements of reliability assessment but also improves the accuracy of reliability interval estimation.

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{\hat{normalp}}_{TEi} = 0.5 / ((), s_{i} + 1)

. Bayesian estimation method is a common method for estimating the failure probability p i , including classical Bayesian method, hierarchical Bayesian method, and E‐Bayesian method.…”

Section: Point Estimate Of Product Reliabilitymentioning

confidence: 99%

Section: Estimates Of Failure Probabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reliability assessment of high‐quality and long‐life products based on zero‐failure data

Xie

et al. 2018

Quality & Reliability Eng

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show abstract

“…Martz and Waller [1] first put forward the Bayesian method to deal with the statistical analysis of the model with zero-failure data. In recent years, it has attracted more and more attention as manufacturing techniques rapidly increase [2][3][4][5]. The Bayesian statistics method has been widely applied in various fields, such as computer science [6], reliability engineering [7][8], safety engineering [9], medical science [10], and finance [11].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Bayesian Reliability Analysis of Binomial Distribution based on Zero-Failure Data

Xiao¹

2018

IJPE

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The aim of this paper is to develop a new hierarchical Bayesian estimation method under symmetric entropy loss function for reliability of the binomial distribution. With the rapid development of manufacturing techniques, some electric products are highly reliable, and thus zero-failure data often occur when putting them in censored lifetime tests. Based on zero-failure data, the reliability analysis is very important for manufacturing. The hierarchical Bayesian estimator is regarded as a robust estimating method, but many existing robust Bayes estimators are complex and difficult to be utilized in practice. The contribution of this article is to present an easy hierarchical Bayesian estimator for reliability of the binomial distribution when reliability has a negative log-gamma prior distribution. Finally, a practical example is provided to show the feasibility and robustness of different estimators.

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Reliability Evaluation of Inverter Based on Accelerated Degradation Test

Qiu

Yang

2018

Lecture Notes in Electrical Engineering

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Reliability Assessment for Very Few Failure Data and Zero-failure Data

Cited by 15 publications

References 0 publications

Reliability assessment of high‐quality and long‐life products based on zero‐failure data

Reliability assessment of high‐quality and long‐life products based on zero‐failure data

Hierarchical Bayesian Reliability Analysis of Binomial Distribution based on Zero-Failure Data

Reliability Evaluation of Inverter Based on Accelerated Degradation Test

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