Reliability engineering dates back to reliability studies in the 20th century; since then, various models have been defined and used. Software engineering plays a key role from several viewpoints, but the main concern is that we're moving toward a more connected world, including enterprises and mobile devices. The three articles in this special issue illustrate current trends in this domain.

Reliability Engineering

In the current study, we have been derived some Basyian estimators for the parameter and relia-bility function of the inverse Rayleigh distribution under Generalized squared error loss function. In order to get the best understanding of the behavior of Bayesian analysis, we consider non-informative prior for the scale parameter using Jefferys prior Information as well as informative prior density represented by Gamma distribution. Monte-Carlo simulation have been employed to compare the behavior of different estimates for the scale parameter and reliability function of in-verse Rayleigh distribution based on mean squared errors and Integrated mean squared errors, respectively. In the current study, we observed that more occurrence of Bayesian estimate using Generalized squared error loss function using Gamma prior is better than other estimates for all cases

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Comparison of Bayes Estimators for Parameter and Relia-bility Function for Inverse Rayleigh Distribution by Using Generalized Square Error Loss Function

كجزء مهم من نظام المعولية بفرض أن  R= P(Y<X<Z) في هذا البحث تمت مناقشة نموذج الاجهاد –المتانة  المتغيرات العشوائية تتبع توزيع معكوس رالي . بعض طرق التقدير التقليدية استخدمت لتخمين المعلمات وهي: طريقة الامكان الاعظم , طريقة العزوم ,طريقة المقدر غير المتحيز ذي اقل تباين وثلاث طرائق من عوامل الاوزان المقلصة . بالاضافة الى ذلك تم استخدام محاكاة مونت كارلو للمقارنة بين طرق التخمين المستخدمة بناء على متوسط مربعات الخطأ.

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Comparison of Some of Estimation methods of Stress-Strength Model: R = P(Y < X < Z)

The present paper concerns with the problem of estimating the reliability system in the stress ? strength model under the consideration non identical and independent of stress and strength and follows Lomax Distribution. Various shrinkage estimation methods were employed in this context depend on Maximum likelihood, Moment Method and shrinkage weight factors based on Monte Carlo Simulation. Comparisons among the suggested estimation methods have been made using the mean absolute percentage error criteria depend on MATLAB program.

https://iopscience.iop.org/article/10.1088/1757-899X/571/1/012038/pdf

On Estimation of the Stress – Strength Reliability Based on Lomax Distribution

In many real-life scenarios, systems frequently perform badly in difficult operating situations. The multiple failures that take place when systems reach their lower, higher, or extreme functioning states typically receive little attention from researchers. This study uses generalized progressive hybrid censoring to discuss the inference of R=P(X<Y<Z) for a component when it is exposed to two stresses, Y,Z, and it has one strength X that is regarded. We assume that both the stresses and strength variables follow an exponentiated exponential distribution with a common scale parameter. We obtain R’s maximum likelihood estimator and approximate confidence intervals. In addition, the Bayesian estimators for symmetric, such as squared error, and asymmetric loss functions, such as linear exponential, are developed. Credible intervals with the highest posterior densities are established. Monte Carlo simulations are used to evaluate and compare the effectiveness of the many proposed estimators. The process is then precisely described using an analysis of real data.

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Bayesian and Non-Bayesian Analysis of Exponentiated Exponential Stress-Strength Model Based on Generalized Progressive Hybrid Censoring Process

Pre-Test Single and Double Stage Shrunken Estimators for the Mean of Normal Distribution with Known Variance

In this study, we consider the system reliability of parallel stress-strength model as an important part of system reliability by assuming the stress and the strengths random variables follows the one parameter Inverse Rayleigh Distribution. Some estimation methods, namely; Maximum Likelihood, Moment method, and Uniformly Minimum Variance Unbiased estimator and three types of shrinkage estimation methods were considered for estimating the system reliability. As well as, Monte Carlo simulation was used to get a comparison among all the suggested methods depends on the statistical indicator Mean Squared Error.

A Comparison for Some of the estimation methods of the Parallel Stress-Strength model In the case of Inverse Rayleigh Distribution

This paper deals with the estimation of the stress strength reliability for a component which has a strength that is independent on opposite lower and upper bound stresses, when the stresses and strength follow Inverse Kumaraswamy Distribution. D estimation approaches were applied, namely the maximum likelihood, moment, and shrinkage methods. Monte Carlo simulation experiments were performed to compare the estimation methods based on the mean squared error criteria.

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On Estimation of P(Y_1<X<Y_2 ) in Cased Inverse Kumaraswamy Distribution

This paper concerns with deriving and estimating the reliability of the multicomponent system in stress-strength model R(s,k), when the stress and strength are identical independent distribution (iid), follows two parameters Exponentiated Pareto Distribution(EPD) with the unknown shape and known scale parameters. Shrinkage estimation method including Maximum likelihood estimator (MLE), has been considered. Comparisons among the proposed estimators were made depending on simulation based on mean squared error (MSE) criteria.

Abbas Najim Salman

Papers

Pre-Test Single and Double Stage Shrunken Estimators for the Mean of Normal Distribution with Known Variance

A Comparison for Some of the estimation methods of the Parallel Stress-Strength model In the case of Inverse Rayleigh Distribution

On Estimation of P(Y_1<X<Y_2 ) in Cased Inverse Kumaraswamy Distribution

On Shrinkage Estimation for R(s, k) in Case of Exponentiated Pareto Distribution

Comparison of Some of Estimation methods of Stress-Strength Model: R = P(Y < X < Z)