Design optimization and redundant dependency study of series k — out — of — n : G repairable systems

Habib, Milia; Yalaoui, Farouk; Chehade, Hicham; Chebbo, Nazir; Jarkass, Iman

doi:10.1016/j.ifacol.2016.11.022

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…Reliability analysis of K-out-of-N: G repairable system with single vacation has been studied by Wu et al (2014). Habib et al (2016) discussed design optimization and redundant dependency study of series K-out-of-N: G repairable system. Kim (2017) developed optimal reliability design of a system with K-out-of-N subsystems considering redundancy strategies.…”

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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“…Subsystem-independence assumption can cause the inaccuracy of the system availability for the multi-component system. Some studies on a series system with k-out-of-n:G subsystems assumed that the subsystems work independently [7,8,11]. When a subsystem fails, the non-failed subsystems are shut off and cannot fail, which is defined as suspended animation (SA) [4,18].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, some researches have studied the availability model of the multi-component series system with k-out-of-n: G subsystems. However, most of the models failed to consider subsystems dependence due to SA [7,8,11]. There are two articles most related to our work considering SA in such a system.…”

Section: Introductionmentioning

confidence: 99%

Availability analysis for a multi-component system with different k-out-of-n:G warm standby subsystems subject to suspended animation

Wang

Guo

Wen

et al. 2019

Eksploatacja i Niezawodność – Maintenance and Reliability

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Industrial equipment or systems are usually constructed as a multi-component series system with k-out-of-n:G subsystems to fulfill a specified function. As a common type of standby, warm standby is considered in the multi-component series system with k-outof-n:G standby subsystems. When a subsystem fails, the non-failed subsystems are shut off and cannot fail, which is defined as suspended animation (SA). If the SA is ignored the non-failed subsystems are assumed to keep working in the SA time, which will cause inaccuracy in the availability analysis for the system. In this paper, we focus on the SA to construct an availability model for a multi-component series system with k-out-of-n:G warm standby subsystems. Multiple continuous time Markov chains are constructed to model the system availability. A Monte Carlo simulation has been carried out to verify our method. Several interesting findings are obtained. 1) The failure rates of subsystems with SA and their limits are derived. 2) The closed-form expressions for the stationary availability of the system and subsystems, mean time to failure, mean time to repair and stationary failure frequency are obtained considering SA. 3) The system stationary availability is a monotone function for its parameters. 4) The SA effect on the stationary availability should be emphasized in two cases, one is both the value of n/k and the failure rate of active components in a k-out-of-n subsystem are relatively large or small, the other is both the value of n/k and the repair rate are relatively small.

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Design optimization and redundant dependency study of series k — out — of — n : G repairable systems

Cited by 2 publications

References 11 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

Availability analysis for a multi-component system with different k-out-of-n:G warm standby subsystems subject to suspended animation

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