Reliability growth analysis of k-out-of-N systems using matrix-based system reliability method

Byun, Ji-Eun; Noh, Hee-Min; Song, Junho

doi:10.1016/j.ress.2017.05.001

Cited by 39 publications

(9 citation statements)

References 19 publications

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“…Rahmani et al (2016) studied the importance of K-out-of-N system with components having random weights. Reliability growth analysis of K-outof-N systems using matrix-based system reliability method has been studied by Byun et al (2017). Kim (2018) evaluated optimal number of components in load sharing system for maximizing reliability.…”

Section: Introductionmentioning

confidence: 99%

Working Vacation Policy for Load Sharing K-out-of-N: G System

Kumar

Gupta

2022

JRSS

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In industrial systems, the K-out-of-N: G system is a prominent type of redundancy. The load sharing protects such system from malfunctioning/destroying and avoids overload problem that affects the system reliability in a significant manner. In this paper we develop a Markovian model of load-sharing K-out-of-N: G system having non-identical repairable components wherein the server may on working vacation. During his vacation period, the server repairs the failed components with different service rates rather than completely terminating service rate. The failed component gets immediately repaired by the server if not on vacation, and unequal load is distributed among remaining surviving components. The lifetime of each component is load dependent followed by non-identical exponential distribution with different failure rates. The system is failed down due to common cause with failure density which is also exponentially distributed. We suggest closed structure analytic expressions for reliability, cost estimation and other performance measures of the load-sharing K-out-of-N: G repairable system by incorporating the concept of working vacation. For the solution aspiration, Runge-Kutta method is utilized to solve the system of differential equations. Furthermore, we perform the numerical analysis for two illustrations 1-out-of-3: G system and 3-out-of-4: G system. The numerical simulation is carried out for the validation of analytical results which are exhibited and compared by giving numerical outcomes and neuro-fuzzy outcomes based on fuzzy interference system with the help of MATLAB.

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Section: Introductionmentioning

confidence: 99%

Working Vacation Policy for Load Sharing K-out-of-N: G System

Kumar

Gupta

2022

JRSS

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“…Computer reliability is one of the key factors taken into account in maintaining the accuracy of the computer. Simply put, software reliability is about system failure or failure [7]. "Success and success" are two distinct variables commonly included in our software development.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Quartile Deviation-based Support Vector Regression Model for Software Reliability Datasets

Reddy¹,

Prasanth²

2022

IJACSA

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Software reliability estimation using machine learning play a major role on the different software quality reliability databases. Most of the conventional software reliability estimation model fails to predict the test samples due to high true positive rate of the traditional support vector regression models. Most of the traditional machine learning based fault prediction models are integrated with standard software reliability growth measures for reliability severity classification. However, these models are used to predict the reliability level of binary class with less standard error. In this paper, a hybrid support vector regression-based quartile deviation growth measure is implemented on the training fault datasets. Experimental results are simulated on various reliability datasets with different configuration parameters for fault prediction.

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“…A series system is equivalent to an n-out-of-n system, while a parallel system is equivalent to a 1-out-of-n system. A series of effective reliability methods of systems have been developed, including the first-order reliability method (FORM) [1], second-order reliability method (SORM) [2], logical differential calculus [3][4][5], Monte Carlo (MC) simulations [6], and so on [7][8][9][10][11][12]. However, most methods have assumed that there are ideal structures with perfectly ductile components.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of k-out-of-n Systems of Components with Potentially Brittle Behavior by Universal Generating Function and Linear Programming

Chang

Mai

et al. 2020

Mathematical Problems in Engineering

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Due to initial cracks, careless construction, and extreme load conditions, components with brittle behavior may exist in a structural system. The presence of brittle behavior of components usually is accompanied by a low strength. However, existing methods for calculating the reliability of structures of components with brittle behavior are rather complicated or impossible. By means of decomposing the entire system into a set of subsystems, this paper proposed a method to estimate the bounds on failure probability of k-out-of-n system of components with potentially brittle behavior by using universal generating function (UGF) and linear programming (LP). Based on the individual component state probabilities and joint probabilities of the states of a small number of components, the proposed method can provide the bounds for the failure probability of a system with a large number of components. The accuracy and efficiency of the proposed method are investigated using numerical examples.

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Reliability growth analysis of k-out-of-N systems using matrix-based system reliability method

Cited by 39 publications

References 19 publications

Working Vacation Policy for Load Sharing K-out-of-N: G System

Working Vacation Policy for Load Sharing K-out-of-N: G System

A Hybrid Quartile Deviation-based Support Vector Regression Model for Software Reliability Datasets

Reliability Analysis of k-out-of-n Systems of Components with Potentially Brittle Behavior by Universal Generating Function and Linear Programming

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