Reliability Prediction of Repairable Redundant System with Imperfect Switching and Repair

Jain, Madhu

doi:10.1007/s13369-015-1865-9

Cited by 27 publications

(2 citation statements)

References 9 publications

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“…A CCF is referred to the failure of some or all components of the system at the same time or successively within a short time interval caused by a single cause, such as design defciency, energetic events, temperature change, humidity, or operator errors. Jain [22] introduced CCFs into a multicomponent mixed standby machining system. Cheng et al [23] obtained the transient indices for any arbitrary initial state and the steady-state availability for a series system with CCFs.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis for a Redundant System with Random Inspection and $R$ Repairmen Under Repair Pressure

Gao

Wang²

2023

Scientific Programming

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This study examines a repairable K -out-of- N + W : G system with warm standby components and R repairmen, where common cause failure (CCF), random inspection, and a repair pressure coefficient are involved. Due to the typical non-self-announcement of the standby component’s failure, performing a random inspection on warm standby components is necessary to find whether there are failed standby components. When the number of failed components is larger than R (heavy loading occurs), all the repairmen increase their repair rates under a repair pressure coefficient to reduce the waiting queue and enhance the reliability and availability of the system. By adopting a matrix-analytic method and Markov renewal theory, this study provides the steady-state distribution and some reliability indices mean time to first failure time (MTTFF) and reliability function from the arbitrary initial state. By some numerical experiments, sensitivity analysis is finally given to show the effect of the system parameters on some performance measures.

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Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis for a Redundant System with Random Inspection and $R$ Repairmen Under Repair Pressure

Gao

Wang²

2023

Scientific Programming

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“…Shree et al (2015) proposed a Markov model for the machining systems with hot spares. Jain (2016) presented the transient study of machining system by incorporating some realistic features, namely service interruption, priority and mixed standbys support. Recently, proposed a Markov model for the repairable system including the features of F-policy, working vacation, and server break down.…”

Section: Introductionmentioning

confidence: 99%

Vacation model for Markov machine repair problem with two heterogeneous unreliable servers and threshold recovery

Jain

Meena

2017

J Ind Eng Int

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Markov model of multi-component machining system comprising two unreliable heterogeneous servers and mixed type of standby support has been studied. The repair job of broken down machines is done on the basis of bi-level threshold policy for the activation of the servers. The server returns back to render repair job when the prespecified workload of failed machines is build up. The first (second) repairman turns on only when the work load of N 1 (N 2 ) failed machines is accumulated in the system. The both servers may go for vacation in case when all the machines are in good condition and there are no pending repair jobs for the repairmen. Runge-Kutta method is implemented to solve the set of governing equations used to formulate the Markov model. Various system metrics including the mean queue length, machine availability, throughput, etc., are derived to determine the performance of the machining system. To provide the computational tractability of the present investigation, a numerical illustration is provided. A cost function is also constructed to determine the optimal repair rate of the server by minimizing the expected cost incurred on the system. The hybrid soft computing method is considered to develop the adaptive neuro-fuzzy inference system (ANFIS). The validation of the numerical results obtained by Runge-Kutta approach is also facilitated by computational results generated by ANFIS.

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Availability Prediction of Repairable Fault-Tolerant System with Imperfect Coverage, Reboot, and Common Cause Failure

Jain¹,

Kumar²

2018

Performance Prediction and Analytics of Fuzzy, Reliability and Queuing Models

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Reliability Prediction of Repairable Redundant System with Imperfect Switching and Repair

Cited by 27 publications

References 9 publications

Sensitivity Analysis for a Redundant System with Random Inspection and $R$ Repairmen Under Repair Pressure

Sensitivity Analysis for a Redundant System with Random Inspection and $R$ Repairmen Under Repair Pressure

Vacation model for Markov machine repair problem with two heterogeneous unreliable servers and threshold recovery

Availability Prediction of Repairable Fault-Tolerant System with Imperfect Coverage, Reboot, and Common Cause Failure

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Reliability Prediction of Repairable Redundant System with Imperfect Switching and Repair

Cited by 27 publications

References 9 publications

Sensitivity Analysis for a Redundant System with Random Inspection and R Repairmen Under Repair Pressure

Sensitivity Analysis for a Redundant System with Random Inspection and R Repairmen Under Repair Pressure

Vacation model for Markov machine repair problem with two heterogeneous unreliable servers and threshold recovery

Availability Prediction of Repairable Fault-Tolerant System with Imperfect Coverage, Reboot, and Common Cause Failure

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Product

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About

Sensitivity Analysis for a Redundant System with Random Inspection and $R$ Repairmen Under Repair Pressure

Sensitivity Analysis for a Redundant System with Random Inspection and $R$ Repairmen Under Repair Pressure