k-out-of-n: G system with repair: the D-policy

Krishnamoorthy, A.; Ushakumari, P. V.

doi:10.1016/s0305-0548(00)00019-8

Cited by 28 publications

(20 citation statements)

References 3 publications

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“…In his paper both possibilities of a k-out-of-n model with sensor in which all the failure can be repaired and another model in which the failures can lead to an absorbing state in which there is no transmission is possible to a functional state is discussed. Krishnamoorthy et al [33,34,35] studied k-out-of-n:G systems with various repair various repair policies. Hoanpham [24] has presented a handbook on reliability engineering.…”

Section: K-out-of-n System With Fault Coveragementioning

confidence: 99%

Reliability Analysis of k-out-of-n: G System: A Short Review

Krishnan¹

2020

IJEAS

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Section: K-out-of-n System With Fault Coveragementioning

confidence: 99%

Reliability Analysis of k-out-of-n: G System: A Short Review

Krishnan¹

2020

IJEAS

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“…where e (4) and e (5) are column matrices given by e (4) = 0 e (6) ⊗ e mm1 e (5) = 0 e (6) ⊗ e m(m1+m2) e (6) = [1, 2, . .…”

Section: System Performance Measuresmentioning

confidence: 99%

RELIABILITY OF A k-OUT-OF-n SYSTEM WITH REPAIR BY A SERVICE STATION ATTENDING A QUEUE WITH POSTPONED WORK

Krishnamoorthy

Narayanan

Deepak³

2007

Int. J. Rel. Qual. Saf. Eng.

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In this paper the reliability of a repairable k-out-of-n system is studied. Repair times of components follow a phase type distribution. In addition, the service facility offers service to external customers which arrive according to a MAP. An external customer, who finds an idle server on its arrival, is immediately selected for service. Otherwise, the external customer joins the queue in a pool of postponed work of infinite capacity with probability 1 if the number of failed components in the system is < M (M ≤ n − k + 1) and if the number of failed components ≥ M it joins the pool with probability γ or leaves the system forever. Repair times of components of the system and that of the external customers have independent phase type distributions. At a service completion epoch if the buffer has less than L customers, a pooled customer is taken for service with probability p, 0 < p < 1 If at a service completion epoch no component of the system is waiting for repair, a pooled customer, if any waiting, is immediately taken for service. We obtain the system state distribution under the condition of stability. A number of performance characteristics are derived. A cost function involving L, M , γ and p is constructed and its behaviour investigated numerically.

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“…Pham and Wang [12] proposed a time based opportunistic maintenance policy named as ( , ) maintenance policy, where and indicate the minimal and perfect repair times, respectively. Krishnamoorthy and Ushakumari [13], Krishnamoorthy et al [14], Krishnamoorthy and Rekha [15] and Ushakumari and Krishnamoorthy [16], Qi et al [17] proposed maintenance policies based on the number of failed components, fix time intervals, and combination of time interval and number of failed components. Ding and Tian [18] reported an opportunistic maintenance policy based on the component's age threshold.…”

Section: Introductionmentioning

confidence: 99%

Wind Power Performance Optimization Considering Redundancy and Opportunistic Maintenance

Sangroudi¹

2018

mendel

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In This paper the redundancy and imperfect opportunistic maintenance optimization of a multi-state weighted k-out-of-n system is formulated. The objective is to determine the k-out-of-n system redundancy level and the maintenance strategy which will minimize the wind farm life cycle cost subject to an availability constraint. A new condition based opportunistic maintenance approach is developed. Different component health state thresholds are introduced for imperfect maintenance of failed subsystems and working subsystems and preventive dispatching of maintenance teams. In addition, a simulation method is developed to evaluate the performance measures of the system considering different types of subsystems, maintenance activation delays and durations, limited number of maintenance teams, and discrete inspection of the system. Also, a multi-seed tabu search heuristic algorithm is also proposed to solve the formulated problem. An application to the optimal design of a wind farm is provided to illustrate the proposed approach.

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k-out-of-n: G system with repair: the D-policy

Cited by 28 publications

References 3 publications

Reliability Analysis of k-out-of-n: G System: A Short Review

Reliability Analysis of k-out-of-n: G System: A Short Review

RELIABILITY OF A k-OUT-OF-n SYSTEM WITH REPAIR BY A SERVICE STATION ATTENDING A QUEUE WITH POSTPONED WORK

Wind Power Performance Optimization Considering Redundancy and Opportunistic Maintenance

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