Augmented Strength Reliability of Equipment Under Gamma Distribution

Chandra, Navin; Sen, Subhradev

doi:10.2991/jsta.2014.13.3.3

Cited by 7 publications

(7 citation statements)

References 7 publications

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“…It is noticed that case-I and case-II of ASP were special cases of case-III, which we call it as generalized case of ASP. The different cases and its detailed discussion of ASP by assuming stress and strength are independently and identically distributed as IG distribution may be referred [8]. The problem of augmenting strength reliability was firstly attempted by [1].…”

Section: Igmentioning

confidence: 99%

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Non Informative Priors for the Stress-Strength Reliability in the Generalized Augmented Inverse Gaussian Distribution

Chandra¹,

Rathaur²

2017

IJPE

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In this paper the Bayesian and classical estimation of augmented strength reliability under Augmentation Strategy Plan (ASP) have been considered. The Augmentation Strategy Plan (ASP) is suggested for enhancing the strength of failed equipment. The Bayes estimation is carried out by assuming the model parameters are random variable and having non-informative type of priors (uniform and Jeffery's priors) under two different loss functions, viz. squared error loss function (SELF) and Linex loss function (LLF). We assume that the Inverse Gaussian stress (Y) is subjected to equipment having Inverse Gaussian strength (X) and are independent to each other. A comparative study between ML and Bayesian estimators have been carried out on the basis of mean square errors (MSE) and absolute biases. The Markov Chain Monte Carlo method of approximations has been applied to draw posterior expectations under both the loss functions. The MSE and absolute biases are calculated with 1000 replications.

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Section: Igmentioning

confidence: 99%

“…They initially derived augmenting strength reliability models for exponential distribution for three different possible cases of ASP. After one decade, [7] pointed out applicability of ASP and attempted augmenting gamma strength reliability of equipment for same set up. Some more references in this direction may be viewed in [9,10].…”

Section: Igmentioning

confidence: 99%

Non Informative Priors for the Stress-Strength Reliability in the Generalized Augmented Inverse Gaussian Distribution

Chandra¹,

Rathaur²

2017

IJPE

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“…The ASP comprises three possible situations to enhance the strength reliability of an equipment to face the common stress. These cases under ASP [11] are stated mathematically as follows:…”

Section: Introductionmentioning

confidence: 99%

“…[12] They initially derived augmenting strength reliability models for exponential distribution for three different possible cases of ASP. After one decade, Chandra and Sen [11] pointed out applicability of ASP and attempted augmenting gamma strength reliability of equipment for same set-up. In similar manner, Chandra and Rathaur [13] studied for augmenting inverse Gaussian stress-strength reliability under ASP.…”

Section: Introductionmentioning

confidence: 99%

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Bayes Estimation of Augmenting Gamma Strength Reliability of a System under Non-informative Prior Distributions

Chandra¹,

Rathaur²

2017

Calcutta Statistical Association Bulletin

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In this article, Bayes estimation of system’s augmented strength reliability is studied under squared-error loss function (SELF) and LINEX loss function (LLF) for the generalized case of augmentation strategy plan (ASP). ASP is helpful in enhancing the strength reliability of weaker system/equipment. It is assumed that the stress (usual) and augmented strength follow a gamma distribution with common shape [Formula: see text] and scale [Formula: see text] parameters. A simulation study is performed for the comparisons of Bayes estimators of augmented strength reliability for non-informative types of prior (uniform and Jeffrey’s priors) with maximum likelihood estimators on the basis of their mean square errors and the absolute biases by simulating 1,000 Monte Carlo samples. The proposed methods are compared by analysing real and simulated datasets for illustration purpose.

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A model of software fault detection and correction processes considering heterogeneous faults

Xie

Hui

Zhai

et al. 2022

Quality & Reliability Eng

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Numerous software reliability growth models (SRGMs) are proposed in the last four decades to track the reliability growth of software during the testing phase. In many existing models, it is assumed that the faults in the software are of the same type. However, this assumption may not be realistic. Software contains different faults and the detection/correction of different faults can require different efforts. In this paper, we propose a more general software reliability model considering heterogeneous faults, where the parameters for the detection time and correction delay of different faults are assumed to be faults specific and allowed to follow arbitrary distributions. Considering different parameters makes the model more flexible and adaptable to different practical software development situations. Some specific models are derived under different assumptions on the distributions of fault detection time, fault correction delay, and the corresponding parameters. Both MLE and LSE are proposed for parameter estimation. Illustrative examples with real data set are studied to compare the proposed models with homogeneous faults model, and the results have shown advantages of our models. The optimal software release policy based on the proposed model is also studied.

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Augmented Strength Reliability of Equipment Under Gamma Distribution

Cited by 7 publications

References 7 publications

Non Informative Priors for the Stress-Strength Reliability in the Generalized Augmented Inverse Gaussian Distribution

Non Informative Priors for the Stress-Strength Reliability in the Generalized Augmented Inverse Gaussian Distribution

Bayes Estimation of Augmenting Gamma Strength Reliability of a System under Non-informative Prior Distributions

A model of software fault detection and correction processes considering heterogeneous faults

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