Estimation of Reliability in Multicomponent Stress-Strength Based on Generalized Rayleigh Distribution

A multicomponent system of k components having strengths following k- independently and identically distributed random variables x1, x2, ..., xk and each component experiencing a random stress Y is considered. The system is regarded as alive only if at least s out of k (s \u3c k) strengths exceed the stress. The reliability of such a system is obtained when strength and stress variates are given by a generalized Rayleigh distribution with different shape parameters. Reliability is estimated using the maximum likelihood (ML) method of estimation in samples drawn from strength and stress distributions; the reliability estimators are compared asymptotically. Monte-Carlo simulation is used to compare reliability estimates for the small samples and real data sets illustrate the procedure

Sampling Plan for Lab Extracted Beverages Data with Multiple Dependent States Using the Odd Log-Logistic Distribution

In this paper, we propose a Multiple Dependent State Sampling (MDSS) plan for decision-making on lot acceptance/rejection based on properties of current and preceding lots sampled. The plan uses the Odd Log-Logistic Generalized Exponentiated (OLLGE) distribution to determine the median life of the product through a truncated-time life test. Optimal parameters such as sample size, acceptance/rejection numbers, and preceding lots are obtained using the operating characteristic curve (OC-curve). The plan’s performance is compared to that of single sampling (SS) plans using Lab Extracted Beverages data sets on carbon dioxide pressure (MPa)

Estimation of Reliability in Multicomponent Stress-strength Based on Generalized Exponential Distribution Estimación de confiabilidad en la resistencia al estrés de multicomponentes basado en la distribución exponencial generalizada

A multicomponent system of k components having strengths following k- independently and identically distributed random variables X1, X2,ldots,Xk and each component experiencing a random stress Y is considered. The system is regarded as alive only if at least s out of k (sk) strengths exceed the stress. The reliability of such a system is obtained when strength and stress variates are given by generalized exponential distribution with different shape parameters. The reliability is estimated using ML method of estimation in samples drawn from strength and stress distributions. The reliability estimators are compared asymptotically. The small sample comparison of the reliability estimates is made through Monte Carlo simulation. Using real data sets we illustrate the procedure.Se considera un sistema de k multicomponentes que tiene resistencias que se distribuyen como k variables aleatorias independientes e idénticamente distribuidas X1, X2,ldots, Xk y cada componente experimenta un estrés aleatorio Y. El sistema se considera como vivo si y solo si por lo menos s de k (s < k) resistencias exceden el estrés. La confiabilidad de este sistema se obtiene cuando las resistencias y el estrés se distribuyen como una distribución exponencial generalizada con diferentes parámetros de forma. La confiabilidad es estimada usando el método ML de estimación en muestras extraídas tanto para distribuciones de resistencia como de estrés. Los estimadores de confiabilidad son comparados asintóticamente. La comparación para muestras pequeñas de los estimadores de confiabilidad se hace a través de simulaciones Monte Carlo. El procedimiento también se ilustra mediante una aplicación con datos reales

A Control Chart for Time Truncated Life Tests Using Exponentiated Half Logistic Distribution

In this article, an exponentiated half logistic distribution considered to develop an attribute control chart for time truncated life tests with known or unknown shape parameter. The performance of the proposed chart is evaluated in terms of average run length (ARL) using the Monte Carlo simulation. The extensive tables are provided for the industrial use for various values of shape parameter, sample size, specified ARL and shift constants. The advantages of the proposed control chart are discussed over the existing truncated life test control charts. The performance of the proposed control chart is also studied using the simulated data sets for industrial purpose

Estimación de confiabilidad en la resistencia al estrés de multicomponentes basado en la distribución exponencial generalizada

Se considera un sistema de k multicomponentes que tiene resistencias que se distribuyen como k variables aleatorias independientes e idénticamente distribuidas X_{1}, X_{2},..., X_{k} y cada componente experimenta un estrés aleatorio Y . El sistema se considera como vivo si y solo si por lo menos s de k(s and lt; k) resistencias exceden el estrés. La confiabilidad de este sistema se obtiene cuando las resistencias y el estrés se distribuyen como una distribución exponencial generalizada con diferentes parámetros de forma. La confiabilidad es estimada usando el método ML de estimación en muestras extraídas tanto para distribuciones de resistencia como de estrés. Los estimadores de confiabilidad son comparados asintóticamente. La comparación para muestras pequeñas de los estimadores de confiabilidad se hace a través de simulaciones Monte Carlo. El procedimiento también se ilustra mediante una aplicación con datos reales.A multicomponent system of k components having strengths following k-independently and identically distributed random variables X_{1}, X_{2},..., X_{k} and each component experiencing a random stress Y is considered. The system is regarded as alive only if at least s out of k (s and lt; k) strengths exceed the stress. The reliability of such a system is obtained when strength and stress variates are given by generalized exponential distribution with different shape parameters. The reliability is estimated using ML method of estimation in samples drawn from strength and stress distributions. The reliability estimators are compared asymptotically. The small sample comparison of the reliability estimates is made through Monte Carlo simulation. Using real data sets we illustrate the procedure

Neutrosophic Log-Logistic Distribution Model in Complex Alloy Metal Melting Point Applications

Abstract The log-logistic distribution is more comprehensively applied in the area of survival and reliability engineering analysis for modeling the lifetime data practices of both human and electronic designs. The goal of this paper is to develop a generalization of the classical pattern log-logistic distribution, known as the neutrosophic log-logistic distribution (NLLD), to model various survival and reliability engineering data with indeterminacies. The developed distribution is especially useful for modeling indeterminate data that is roughly positively skewed. This paper discusses the developed NLLD’s main statistical properties such as neutrosophic survival function, neutrosophic hazard rate, neutrosophic moments, and neutrosophic mean time failure. Furthermore, the neutrosophic parameters are estimated using the well-known maximum likelihood (ML) estimation method in a neutrosophic environment. A simulation study is carried out to establish the achievement of the estimated neutrosophic parameters. As a final point, the proposed NLLD applications in the real world have been discussed with the help of real data. The real data illustrated that the efficiency of the proposed model as compared with the existing models

Estimation of Reliability in Multicomponent Stress-strength Based on Generalized Exponential Distribution

Abstract A multicomponent system of k components having strengths following kindependently and identically distributed random variables X1, X2, . . . , X k and each component experiencing a random stress Y is considered. The system is regarded as alive only if at least s out of k (s &lt; k) strengths exceed the stress. The reliability of such a system is obtained when strength and stress variates are given by generalized exponential distribution with different shape parameters. The reliability is estimated using ML method of estimation in samples drawn from strength and stress distributions. The reliability estimators are compared asymptotically. The small sample comparison of the reliability estimates is made through Monte Carlo simulation. Using real data sets we illustrate the procedure. Key words: Asymptotic confidence interval, Maximum likelihood estimation, Reliability, Stress-strength model. Resumen Se considera un sistema de k multicomponentes que tiene resistencias que se distribuyen como k variables aleatorias independientes e idéntica-mente distribuidas X1, X2, . . . , X k y cada componente experimenta un estrés aleatorio Y . El sistema se considera como vivo si y solo si por lo menos s de k(s &lt; k) resistencias exceden el estrés. La confiabilidad de este sistema se obtiene cuando las resistencias y el estrés se distribuyen como una distribución exponencial generalizada con diferentes parámetros de forma. La confiabilidad es estimada usando el método ML de estimación en muestras extraídas tanto para distribuciones de resistencia como de estrés. Los estimadores de confiabilidad son comparados asintóticamente. La comparación a Professor. E-mail: [email protected] 68 Gadde Srinivasa Rao para muestras pequeñas de los estimadores de confiabilidad se hace a través de simulaciones Monte Carlo. El procedimiento también se ilustra mediante una aplicación con datos reales. Palabras clave: confiabilidad, estimación máximo verosímil, intervalos de confianza asintóticos, modelo de resistencia-estrés

Estimation of Reliability in Multicomponent Stress-strength Based on Generalized Exponential Distribution

Abstract A multicomponent system of k components having strengths following kindependently and identically distributed random variables X1, X2, . . . , X k and each component experiencing a random stress Y is considered. The system is regarded as alive only if at least s out of k (s &lt; k) strengths exceed the stress. The reliability of such a system is obtained when strength and stress variates are given by generalized exponential distribution with different shape parameters. The reliability is estimated using ML method of estimation in samples drawn from strength and stress distributions. The reliability estimators are compared asymptotically. The small sample comparison of the reliability estimates is made through Monte Carlo simulation. Using real data sets we illustrate the procedure. Key words: Asymptotic confidence interval, Maximum likelihood estimation, Reliability, Stress-strength model. Resumen Se considera un sistema de k multicomponentes que tiene resistencias que se distribuyen como k variables aleatorias independientes e idéntica-mente distribuidas X1, X2, . . . , X k y cada componente experimenta un estrés aleatorio Y . El sistema se considera como vivo si y solo si por lo menos s de k(s &lt; k) resistencias exceden el estrés. La confiabilidad de este sistema se obtiene cuando las resistencias y el estrés se distribuyen como una distribución exponencial generalizada con diferentes parámetros de forma. La confiabilidad es estimada usando el método ML de estimación en muestras extraídas tanto para distribuciones de resistencia como de estrés. Los estimadores de confiabilidad son comparados asintóticamente. La comparación a Professor. E-mail: [email protected] 68 Gadde Srinivasa Rao para muestras pequeñas de los estimadores de confiabilidad se hace a través de simulaciones Monte Carlo. El procedimiento también se ilustra mediante una aplicación con datos reales. Palabras clave: confiabilidad, estimación máximo verosímil, intervalos de confianza asintóticos, modelo de resistencia-estrés