|
|
|
 |
Search published articles |
 |
|
Showing 6 results for khanjari sadegh
Majid Chahkandi, Jalal Etminan, Mohammad Khanjari Sadegh,
Volume 15, Issue 1 (9-2021)
Abstract
Redundancy and reduction are two main methods for improving system reliability. In a redundancy method, system reliability can be improved by adding extra components to some original components of the system. In a reduction method, system reliability increases by reducing the failure rate at all or some components of the system. Using the concept of reliability equivalence factors, this paper investigates equivalence between the reduction and redundancy methods. A closed formula is obtained for computing the survival equivalence factor. This factor determines the amount of reduction in the failure rate of a system component(s) to reach the reliability of the same system when it is improved. The effect of component importance measure is also studied in our derivations.
Jalal Etminan, Mohammad Khanjari Sadegh, Maid Chahkandi,
Volume 16, Issue 2 (3-2023)
Abstract
This paper considers series and parallel systems with independent and identically distributed component lifetimes. The reliability of these systems can be improved by using the reduction method. In the reduction method, system reliability is increased by reducing the failure rates of some of its components by a factor 0<ρ<1, called the equivalent reliability factor. Closed formulas are obtained for some reliability equivalence factors. In comparisons among the performance of the systems, these factors are helpful. We discuss that the reduction method can be considered as a particular case of the proportional hazard rates (PHR) model. Sufficient conditions for the relative aging comparison of the improved series and parallel systems under the PHR model and reduction method are also developed.
Mr. Ali Rostami, Dr. Mohammad Khanjari Sadegh, Dr. Mohammad Khorashadizadeh,
Volume 16, Issue 2 (3-2023)
Abstract
In this article, we consider the estimation of R{r,k}= P(X{r:n1} < Y{k:n2}), when the stress X and strength Y are two independent random variables from inverse Exponential distributions with unknown different scale parameters. R{r,k} is estimated using the maximum likelihood estimation method, and also, the asymptotic confidence interval is obtained. Simulation studies and the performance of this model for two real data sets are presented.
Mahdieh Mozafari, Mohammad Khanjari Sadegh, , Gholamreza Hesamian,
Volume 17, Issue 1 (9-2023)
Abstract
In this paper, some reliability concepts have been investigated based on the α-pessimistic and its relationship with the α-cut of a fuzzy number. For this purpose, if the lifetime distribution of the system components is known, using the definition of the scale fuzzy random variable, based on α-pessimistic, some reliability criteria have been investigated. Also, suppose the lifetime distribution of the components is unknown or only the fuzzy observations of the lifetime of the features are available. In that case, the empirical distribution function of the fuzzy data is used to estimate the reliability, and some examples are provided to illustrate the results.
Ali Rostami, Mohammad Khanjari Sadegh, Mohammad Khorashadizadeh,
Volume 17, Issue 1 (9-2023)
Abstract
This article considers the stress-strength reliability of a coherent system in the state of stress at the component level. The coherent series, parallel and radar systems are investigated. For 2-component series or parallel systems and radar systems, this reliability based on Exponential distribution is estimated by maximum likelihood, uniformly minimum variance unbiased and Bayes methods. Also, simulation studies have been done to check estimators' performance, and real data are analyzed.
Miss. Mahdieh Mozafari, Dr. Mohammad Khanjari Sadegh, Dr. Mohammad Ghasem Akbari, Dr. Gholamreza Hesamian,
Volume 18, Issue 1 (8-2024)
Abstract
In this paper, fuzzy order statistics are expressed based on the concept of α-value, and some of its applications in reliability have been examined. For this purpose, if the lifetime distribution of the system components is known, some of the reliability criteria of the $i$th order statistic using the definition of a fuzzy random variable based on the α-value have been investigated. Also, if the lifetime distribution of the components is unknown or only the fuzzy observations of the lifetime of the components are available, the empirical distribution function of the fuzzy data is used to estimate the reliability based on ordinal statistics, and examples are provided to illustrate the results.
|
|
