On multinormal integrals by Importance Sampling for parallel system reliability

2011 ◽  
Vol 33 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Edoardo Patelli ◽  
Helmut J. Pradlwarter ◽  
Gerhart I. Schuëller
Keyword(s):  
Importance Sampling ◽  
System Reliability ◽  
Parallel System

Reliability Assessment of Power System Using Importance Sampling Technique Based on Layer Optimization Simulation

2011 ◽  
Vol 88-89 ◽  
pp. 554-558 ◽  
Author(s):  
Bin Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power System ◽  
Importance Sampling ◽  
System Reliability ◽  
Sampling Method ◽  
Sampling Technique ◽  
Test System ◽  
Importance Sampling Method ◽  
Importance Sampling Technique

An improved importance sampling method with layer simulation optimization is presented in this paper. Through the solution sequence of the components’ optimum biased factors according to their importance degree to system reliability, the presented technique can further accelerate the convergence speed of the Monte-Carlo simulation. The idea is that the multivariate distribution’ optimization of components in power system is transferred to many steps’ optimization based on importance sampling method with different optimum biased factors. The practice is that the components are layered according to their importance degree to the system reliability before the Monte-Carlo simulation, the more forward, the more important, and the optimum biased factors of components in the latest layer is searched while the importance sampling is carried out until the demanded accuracy is reached. The validity of the presented is verified using the IEEE-RTS79 test system.

scholarly journals KAJIAN RELIABILITAS PADA SISTEM SERI-PARALEL DENGAN EMPAT KOMPONEN

2018 ◽  
Vol 7 (1) ◽  
pp. 73-83
Author(s):  
Farhah Izzatul Jannah ◽  
Sudarno Sudarno ◽  
Alan Prahutama
Keyword(s):  
Weibull Distribution ◽  
System Reliability ◽  
Failure Time ◽  
Likelihood Estimation ◽  
Parallel System ◽  
Operating Conditions ◽  
Failure Time Data ◽  
Time Data ◽  
Rank Regression ◽  
Load Resistor

Reliability analysis is the analysis of the possibility that the product or service will function properly for a certain period of time under operating conditions without failure. One configuration of components that can be formed is a series-parallel system on a filter capacitor circuit using 4 components consisting of 2 rectifier diodes, 1 capacitor, and 1 load resistor. The data used to obtain the value of system reliability is the time of failure based on the assumption of failure of the independent component. The function of the form on the system can be expressed by Ф(x)= x1x3 + x1x4 + x2x3 + x2x4 - x1x3x4 - x2x3x4 - x1x2x3 - x1x2x4 + x1x2x3x4. The parameter values of each distribution are calculated using the Median Rank Regression Estimation (MRRE) and Maximum Likelihood Estimation (MLE) methods. To test the data following a certain distribution or not, the calculation is manually done with the Anderson-Darling (AD) test so that it is known that the failure time data of rectifier diode 1 follows the weibull distribution with parameters  and , failure time data of rectifier diode 2 follows weibull distribution with parameters  and , failure time data of capacitors follow normal distribution with parameters  and , and the failure time data of the load resistor following the gamma distribution with parameters  and . From the calculation of system reliability, it shows that the higher the intensity of the system fails it will affect the value of reliability to be lower. A serial system from a parallel system functions if there is at least one component j in one subsystem that functions. Keywords: Reliability, Series-Parallel, MRRE, MLE, AD.

Appraisal of a Novel Reliability Optimisation Model Using Hypothetical Problems

2014 ◽  
Vol 12 ◽  
pp. 1-14 ◽  
Author(s):  
Stephen B. Twum ◽  
Elaine Aspinwall
Keyword(s):  
Lower Bounds ◽  
System Reliability ◽  
Convex Combination ◽  
Parallel Systems ◽  
Parallel System ◽  
Weighting Scheme ◽  
Optimisation Algorithm ◽  
Level Of Difficulty ◽  
Optimisation Model ◽  
Sensitive Parameters

This paper examines further a novel series-parallel system reliability optimisation model and methodology earlier developed by the authors. The aim was to investigate how the model constructed for hypothetical series-parallel systems, reacted to slight variations to its parameters, which are the weights for the subsystem reliabilities, the feasibility factors which quantify the level of difficulty of improving a component’s reliability, and the subsystem reliability lower bounds. The optimisation algorithm based on a convex combination of the subsystem reliabilities was run using various combinations of the values of these parameters. The results show that the model was stable under the weighting scheme used and the most sensitive parameters were the feasibility factors and the subsystem reliability lower bounds.

More on optimal allocation of components in coherent systems

1996 ◽  
Vol 33 (02) ◽  
pp. 548-556 ◽  
Author(s):  
Fan C. Meng
Keyword(s):  
System Reliability ◽  
Binary Systems ◽  
Optimal Allocation ◽  
Reliability Theory ◽  
Parallel System ◽  
System Level ◽  
Coherent Systems ◽  
Component Level ◽  
Multistate Systems ◽  
Permutation Equivalent

More applications of the principle for interchanging components due to Boland et al. (1989) in reliability theory are presented. In the context of active redundancy improvement we show that if two nodes are permutation equivalent then allocating a redundancy component to the weaker position always results in a larger increase in system reliability, which generalizes a previous result due to Boland et al. (1992). In the case of standby redundancy enhancement, we prove that a series (parallel) system is the only system for which standby redundancy at the component level is always more (less) effective than at the system level. Finally, the principle for interchanging components is extended from binary systems to the more complicated multistate systems.

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