Availability Modeling of Generalized $k$ -Out-of- $n$ :G Warm Standby Systems With PEPA

Xiao-yue, WU; Hillston, Jane; Feng, Cheng

doi:10.1109/tsmc.2016.2563407

Cited by 18 publications

(14 citation statements)

References 36 publications

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“…Block 𝐵 S V ,S IV : The transition 𝑆 𝑉 → 𝑆 𝐼𝑉 indicates that the repair process in the working vacation period is completed according to vector 𝑇 (0) and that the repaired component is reactivated following the initial probability vector 𝛼 before the repairman interrupts vacation according to vector ⃗ 1 𝐷 1 to start repairing another failed component in the regular repair mode following vector 𝛽 (1) .…”

Section: Infinitesimal Generatormentioning

confidence: 99%

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A Markovian analytical approach to a repairable system under the mixed redundancy strategy with a repairman

Juybari

Hamadani

Liu

2022

Quality & Reliability Eng

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The mixed redundancy strategy is one of the most powerful techniques for improving system performance. This novel strategy has, however, been mostly studied in non-repairable redundant systems, while the present study aims to study, for the first time, its application to a repairable system with a repairman in availability and maintainability perspectives. In this system, a repairman is responsible for providing the repair services. The intended system simultaneously benefits from the repairman's vacation concept following different policies, namely: vacation interruption and multiple vacations. Moreover, the system consists of two types of repairs, specifically: regular and working vacation repair. Also, successive vacation times are governed by the Markovian arrival process. Accordingly, the system is analyzed by modeling a vector-valued Markov process and several performance measures are derived in both transient and stationary regimes following the matrix-analytic procedure. Finally, the performance of the proposed approach is verified using numerical examples that involve a system with three repairable non-identical components under the mixed strategy as well as a micro-grid system with three parallel subsystems as a real-life case study.

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Section: Infinitesimal Generatormentioning

confidence: 99%

“…𝑛 𝐴 , 𝑛 𝑆 Numbers of active and standby components, respectively. 𝛽 (1) , 𝛽 (2) Initial state probabilities of regular and working vacation repairs, respectively.…”

Section: Notationsmentioning

confidence: 99%

A Markovian analytical approach to a repairable system under the mixed redundancy strategy with a repairman

Juybari

Hamadani

Liu

2022

Quality & Reliability Eng

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“…6 Warm standby is often used to balance the system economic efficiency and recovery time, since warm standby components can be switched into active states faster than cold standby components, and they have smaller degradation or failure rates compared with active components. 7 Therefore, warm standby systems have been investigated from various aspects in recent years. Amari et al 8 studied reliability characteristics of the warm standby system, whose components were identical and subject to exponential lifetimes.…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis and state transfer scheduling optimization of degrading load-sharing system equipped with warm standby components

Ruan

Lin

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Standby redundancy can meet system safety requirements in industries with high reliability standards. To evaluate reliability of standby systems, failure dependency among components has to be considered especially when systems have load-sharing characteristics. In this paper, a reliability analysis and state transfer scheduling optimization framework is proposed for the load-sharing 1-out-of- N: G system equipped with M warm standby components and subject to continuous degradation process. First, the system reliability function considering multiple dependent components is derived in a recursive way. Then, a Monte Carlo method is developed and the closed Newton-Cotes quadrature rule is invoked for the system reliability quantification. Besides, likelihood functions are constructed based on the measurement information to estimate the model parameters of both active and standby components, whose degradation paths are modeled by the step-wise drifted Wiener processes. Finally, the system state transfer scheduling is optimized by the genetic algorithm to maximize the system reliability at mission time. The proposed methodology and its effectiveness are illustrated through a case study referring to a simplified aircraft hydraulic system.

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“…sciENcE aNd tEchNology tive components and standby components [23]. A standby component switches into the active state upon an active component failure [29]. Warm standby has a general expression for the system reliability and availability.…”

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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Availability Modeling of Generalized $k$ -Out-of- $n$ :G Warm Standby Systems With PEPA

Cited by 18 publications

References 36 publications

A Markovian analytical approach to a repairable system under the mixed redundancy strategy with a repairman

A Markovian analytical approach to a repairable system under the mixed redundancy strategy with a repairman

Reliability analysis and state transfer scheduling optimization of degrading load-sharing system equipped with warm standby components

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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