Approximate confidence limits of the reliability performances for a cold standby series system

2005 ◽  
Vol 19 (1-2) ◽  
pp. 439-445 ◽  
Author(s):  
Yimin Shi ◽  
Xiaolin Shi ◽  
Yong Xu
Keyword(s):  
Series System ◽  
Confidence Limits ◽  
Cold Standby

Bayesian estimation of reliability indexes for cold standby system under general progressive Type II censored data

2014 ◽  
Vol 31 (3) ◽  
pp. 311-343 ◽  
Author(s):  
D.R. Barot ◽  
M.N. Patel
Keyword(s):  
Empirical Bayes ◽  
Series System ◽  
Type Ii ◽  
Confidence Limits ◽  
Content Type ◽  
Type Ii Censoring ◽  
Progressive Type ◽  
Cold Standby ◽  
Censoring Scheme ◽  
Progressive Type Ii Censoring

Purpose – This paper aims to deal with the estimation of the empirical Bayesian exact confidence limits of reliability indexes of a cold standby series system with (n+k−1) units under the general progressive Type II censoring scheme. Design/methodology/approach – Assuming that the lifetime of each unit in the system is identical and independent random variable with exponential distribution, the exact confidence limits of the reliability indexes are derived by using an empirical Bayes approach when an exponential prior distribution of the failure rate parameter is considered. The accuracy of these confidence limits is examined in terms of their coverage probabilities by means of Monte-Carlo simulations. Findings – The simulation results show that accuracy of exact confidence limits of reliability indexes of a cold standby series system is efficient. Therefore, this approach is good enough to use for reliability practitioners in order to improve the system reliability. Practical implications – When items are costly, the general progressive Type II censoring scheme is used to reduce the total test time and the associated cost of an experiment. The proposed method provides the means to estimate the exact confidence limits of reliability indexes of the proposed cold standby series system under this scheme. Originality/value – The application of the proposed technique will help the reliability engineers/managers/system engineers in various industrial and other setups where a cold standby series system is widely used.

Joint optimization of components redundancy, spares inventory and repairmen allocation for a standby series system

2018 ◽  
Vol 233 (4) ◽  
pp. 623-638
Author(s):  
Xian Zhao ◽  
Jing Zhang ◽  
Xiaoyue Wang
Keyword(s):  
Optimal Solution ◽  
Optimal Number ◽  
Joint Optimization ◽  
Series System ◽  
Inventory Policy ◽  
System Availability ◽  
Cold Standby ◽  
Batch Ordering ◽  
The Mathematical Model ◽  
Warm Standby

In the past, redundancy, inventory and maintenance are often considered separately to improve the system availability. Recently, a few joint optimization papers have emerged, but they only considered hot-standby redundancy, one-for-one-ordering inventory policy and the case of single repairman. In order to deepen the previous research, this article formulates a joint optimization model of components redundancy, spares inventory and repairmen allocation for a standby series system with an objective of maximizing system availability. As to the components redundancy, hot-standby, warm-standby and cold-standby are considered, respectively. A more general batching ordering inventory policy is adopted and the number of repairmen is added as a new element. By continuous time Markov process, related reliability probability indices are derived. Then the mathematical model is constructed and branch-and-bound method is employed to solve the optimal solution. Finally, we conduct analysis and comparison for different numerical examples and obtain the following results: cold-standby and warm-standby redundancy yield higher availabilities compared to hot-standby under the same conditions. The optimal batch ordering inventory policy is better than optimal one-for-one-ordering inventory policy if the spares replenishment rate is not high enough. Moreover, an optimal number of repairmen, which is always more than one, can be achieved to enhance the efficiency of maintenance.

Reliability Estimation for Cold Standby Series System Based on General Progressive Type II Censored Samples

2013 ◽  
Vol 321-324 ◽  
pp. 2460-2463 ◽  
Author(s):  
Yi Min Shi ◽  
Xiao Lin Shi
Keyword(s):  
Bayesian Estimation ◽  
Reliability Function ◽  
Reliability Estimation ◽  
Likelihood Method ◽  
Series System ◽  
Type Ii ◽  
Censored Samples ◽  
Progressive Type ◽  
Cold Standby ◽  
Two Parameter

Suppose that the life of unit is distributed as two-parameter exponential distribution. The Bayesian estimation for cold standby series system is studied based on general Progressive type II censored samples. Under the different error loss, the Bayesian estimation of the unknown parameter and reliability function are derived where hyper-parameters are estimated by using Maximum likelihood method. At last, a numerical example is given by means of the Monte-Carlo simulation to illustrate the correctness and feasibility for the method proposed in this paper.

Reliability Analysis of a Cold Standby System under Progressive Type-II Censoring Date

2012 ◽  
Vol 459 ◽  
pp. 540-543 ◽  
Author(s):  
Feng Li
Keyword(s):  
Confidence Interval ◽  
Empirical Bayes ◽  
Reliability Function ◽  
Type Ii ◽  
Confidence Limits ◽  
Average Life ◽  
Type Ii Censoring ◽  
Progressive Type ◽  
Cold Standby ◽  
Progressive Type Ii Censoring

the exact confidence interval estimations and the approximate confidence interval estimations of the reliability indexes for a cold standby series system are investigated under progressive type-II censoring date, the formulae to calculate the exact confidence limits and the empirical Bayes approximate confidence limits of the failure rate, and the reliability function and average life are given. In order to investigate the accuracy of estimations, an illustrative example is examined numerically by means of Monte-Carlo simulation.

scholarly journals Solving Cold-Standby Reliability-Redundancy Allocation Problems with Particle-Based Simplified Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chia-Ling Huang ◽  
Yunzhi Jiang ◽  
Wei-Chang Yeh
Keyword(s):  
Particle Swarm Optimization ◽  
System Reliability ◽  
State Of The Art ◽  
Series System ◽  
Swarm Optimization ◽  
Redundancy Allocation ◽  
System A ◽  
Cold Standby ◽  
Allocation Problems ◽  
Intelligence Technique

Particle swarm optimization (PSO) and simplified swarm optimization (SSO) are two of the state-of-the-art swarm intelligence technique that is widely utilized for optimization purposes. This paper describes a particle-based simplified swarm optimization (PSSO) procedure which combines the update mechanisms (UMs) of PSO and SSO to determine optimal system reliability for reliability-redundancy allocation problems (RRAPs) with cold-standby strategy while aimed at maximizing the system reliability. With comprehensive experimental test on the typical and famous four benchmarks of RRAP, PSSO is compared with other recently introduced algorithms in four different widely used systems, i.e., a series system, a series-parallel system, a complex (bridge) system, and an overspeed protection system for a gas turbine. Finally, the results of the experiments demonstrate that the PSSO can effectively solve the system of RRAP with cold-standby strategy and has good performance in the system reliability obtained although the best system reliability is not obtained in all four benchmarks.

Sign in / Sign up

Export Citation Format

Share Document