Reliability Analysis of 6-Component Star Markov Repairable System with Spatial Dependence

Wang, Liying; Yang, Qing; Tian, Yuran

doi:10.1155/2017/9728019

Cited by 10 publications

(14 citation statements)

References 21 publications

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“…They also presented cost analysis and cost‐benefit analysis based on mean time to failure and steady state availability. Wang et al presented a Vector‐Markov process to describe the performance of Star repairable systems with spatial dependence that consists of a center component and several peripheral components. To consider redundancy allocation and spare parts provisioning simultaneously, Xie et al first developed CTMC models for a single repairable k‐out‐of‐n system under different shutoff rules and then extended the models to series systems consisting of multiple repairable k‐out‐of‐n subsystems.…”

Section: Literature Reviewmentioning

confidence: 99%

A continuous‐time Markov chain model for redundancy allocation problem: An economic analysis

Tohidi

Chavoshi

Bahmaninezhad

2019

Quality & Reliability Eng

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Reliability of an engineering system can be improved by investing on redundant (spare) parts. However, the cost-efficiency of such an investment is a significant concern that needs to be taken into consideration in practice. To do so, a continuous-time Markov chain (CTMC) model is presented in this paper to analyze the system's reliability by allocating redundant components. The developed model can also capture the system's repair and failure conditions by defining appropriate states in CTMC. Subsequently, the net present value (NPV) approach is utilized for a variety of scenarios to investigate the effectiveness of investment on spare parts using the break-even point (BEP) analysis. Afterwards, a comprehensive analysis is carried out to examine the impact of input parameters including interest rate, initial cost of investment, and periodic profit on the decision making process to find the optimal number of spare parts. KEYWORDS break-even point (BEP), continuous-time Markov chain (CTMC), cold standby components, net present value (NPV), redundancy allocation, system reliability 1866

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Section: Literature Reviewmentioning

confidence: 99%

A continuous‐time Markov chain model for redundancy allocation problem: An economic analysis

Tohidi

Chavoshi

Bahmaninezhad

2019

Quality & Reliability Eng

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“…Military exploitation systems are largely based on a plan-preventive maintenance strategy, aiming to maximize the technical availability of facilities [6][7][8][9]. This strategy assumes the implementation of maintenance with a specific labor intensity, in accordance with the required scope.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Light Utility Vehicle Readiness in Military Transportation Systems Using Markov and Semi-Markov Processes

2022

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This paper presents the issues of modeling the operation process of light utility vehicles operating in military transport systems. The required condition for the effective operation of the system is to maintain the means of transport at the appropriate level of technical readiness. For this purpose, it is necessary to equip the technical system with appropriate resources enabling the efficient implementation of fuel refilling, maintenance and repair processes. Each failure of the means of transport causes a significant reduction in transport capacity, which then results in the inability to perform the planned tasks. Quality control and vehicle operation process management require advanced mathematical methods and tools. Three indicators have been proposed as quantitative characteristics for assessing and optimizing the availability of military vehicles: functional readiness, technical efficiency and airworthiness. To determine their value, a stochastic exploitation model was developed based on the application of the theory of Markov processes. Based on the collected empirical data, a nine-state phase space of the studied process was identified. Operating states were distinguished relating to the implementation of the transport task, refueling, parking in the garage, as well as maintenance and repairs. As part of the considerations for the continuous time, verification of the distributions of time characteristics led to the development of a semi-Markov model. The ergodic probabilities calculated based on the conditional probability matrix of interstate transitions and the expected values of the time spent in the states were used to determine the indicators of functional availability, efficiency and technical suitability. In order to determine the possibility of optimizing the process, a sensitivity analysis was performed. Reducing the amount of time the vehicles must wait for repair by about 50% can improve the values of the indexes from 0.91 to 0.95.

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“…The identification of critical components in the machine is achieved through failure and repair rates of components in the corresponding machine state. A Markov process-implemented repairable system has spatial dependence [4]. Spatial dependence is found in a repairable system where components are arranged as a star or lattice connection, where the central component N is connected to the peripheral components N-1.…”

Section: Introductionmentioning

confidence: 99%

Proactive Maintenance Model Using Reinforcement Learning Algorithm in Rubber Industry

Chandran¹,

Sudhakarapandian

2022

Processes

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This paper presents an investigation into the enhancement of availability of a curing machine deployed in the rubber industry, located in Tamilnadu in India. Machine maintenance is a major task in the rubber industry, due to the demand for product. Critical component identification in curing machines is necessary to prevent rapid failure followed by subsequent repairs that extend curing machine downtime. A reward in the Reinforcement Learning Algorithm (RLA) prevents frequent downtime by improving the availability of the curing machine at time when unscheduled long-term maintenance would interfere with operation, due to the occurrence of unspecified failure to a critical component. Over time, depreciation and degradation of components in a machine are unavoidable, as is shown in the present investigation through intelligent assessment of the lifespan of components. So far, no effective methodology has been implemented in a real-time maintenance environment. RLAs seem to be a more effective application when it is based on intelligent assessment, which encompasses the failure and repair rate used to calculate availability in an automated environment. Training of RLAs is performed to evaluate overall equipment efficiency (OEE) in terms of availability. The availability of a curing machine in the form of state probability is modeled in first-order differential-difference equations. RLAs maximize the rate of availability of the machine. Preventive maintenance (PM) rate for four modules of 16 curing machines is expressed in a transition diagram, using transition rate. Transition rate indicates the degree of PM and unplanned maintenance rates that defines the total availability of the four modules. OEE is expressed in terms of the availability of curing machines, which is related to performance and quality. The results obtained by RLA are promising regarding short-term and long-term efficiencies of OEE, which are 95.19% and 83.37%, respectively.

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Reliability Analysis of 6-Component Star Markov Repairable System with Spatial Dependence

Cited by 10 publications

References 21 publications

A continuous‐time Markov chain model for redundancy allocation problem: An economic analysis

A continuous‐time Markov chain model for redundancy allocation problem: An economic analysis

Analysis of Light Utility Vehicle Readiness in Military Transportation Systems Using Markov and Semi-Markov Processes

Proactive Maintenance Model Using Reinforcement Learning Algorithm in Rubber Industry

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