• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.735-740
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• 2008

Recently, the research of PM (Preventive Maintenance) method on the RCM(Reliability-Centered Maintenance) of the system equipment is being actively advanced for a few years. For the most of the current power equipment maintenance, the state of the equipment after maintenance is assumed to be becoming 'as good as new ones' state. However, the state of the power equipment is maintained like the states of the between 'as good as new ones' and 'as bad as old ones' by imperfect maintenance that implies the life decrease of the equipment by frequent breakdown, the error of maintenance process, and so on. So, the Maintenance method considering the real case has to reflect Imperfect maintenance than perfect maintenance. This paper suggests the Preventive Maintenance method by using Quasi - Renewal Theory for the gas turbine equipment as deliberating the imperfect maintenance for the real cases.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.87-96
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• 2019

Purpose: The purpose of this paper is to determine an optimal production time for economic production quantity model with preventive maintenance and random defective rate as the function of a machinery deteriorates. Methods: If a machinery shifts from "in-control" state to "out-of-control" state, a proportion of defective items being produced increases. It is assumed that time to state shift is a random variable and follows an arbitrary distribution. The elapsed time until process shift decreases stochastically as a production cycle repeats and quasi-renewal process is used to implement for production facilities to deteriorate. Results: When the exponential parameter for exponential distribution increases, the optimal production time increases. When Weibull distribution is considered, the optimal production time is closely affected by the shape parameter of Weibull distribution. Conclusion: A mathematical model is suggested to find optimal production time and optimal number of production cycles and numerical examples are implemented to validate the patterns for changes of optimal times under different parameters assumptions. The real application is implemented using the proposed approach.