• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.8
• /
• pp.1305-1310
• /
• 2008

Traditional maintenance planning is based on a constant maintenance interval for equipment life. In order to consider economic aspect for time based preventive maintenance, preventive maintenance is desirable to be scheduled by RCM(Reliability-Centered Maintenance) evaluation. The main objective of RCM is to reduce the maintenance cost, by focusing on the most important functions of the system and avoiding or removing maintenance actions that are not strictly necessary. So, Markov state model is utilized considering stochastic state in RCM. In this paper, a Markov state model which can be used for scheduling and optimization of maintenance is presented. The deterioration process of system condition is modeled by the stepwise Markov model in detail. Also, because the system is not continuously monitored, the inspection is considered. In case study, simulation results about RCM will be shown using the real historical data of combustion turbine generating unit in Korean power systems.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.3
• /
• pp.234-240
• /
• 1999

This paper presents a new approach for large-scale generator maintenance scheduling optimizations. The generator preventive maintenance scheduling problems are typical discrete dynamic n-dimensional vector optimization ones with several inequality constraints. The considered objective function to be minimized a subset of{{{{ { R}^{n } }}}} space is the variance (i.g., second-order momentum) of operating reserve margin to levelize risk or reliability during a year. By its nature of the objective function, the optimal solution can only be obtained by enumerating all combinatorial states of each variable, a task which leads to computational explosion in real-world maintenance scheduling problems. This paper proposes a new priority search mechanism based on each generator's discrete sensitivity value which was analytically developed in this study. Unlike the conventional capacity-based priority search, it can prevent the local optimal trap to some extents since it changes dynamically the search tree in each iteration. The proposed method have been applied to two test systems (i.g., one is a sample system with 10 generators and the other is a real-world lage scale power system with 280 generators), and the results anre compared with those of the conventional capacith-based search method and combinatorial optimization method to show the efficiency and effectiveness of the algorithm.

• IE interfaces
• /
• v.25 no.1
• /
• pp.127-133
• /
• 2012

This paper considers the preventive maintenance scheduling problem of the cluster tool which is one of the most important manufacturing equipments in the next-generation semiconductor production environment. We define a random process that expresses the successive amount of chemicals accumulating inside the tool. Based on the renewal theory, we find the expected value and probability distribution of the time that the amount of accumulated chemicals exceeds a predetermined level. For a given probability that the accumulated chemicals exceeds the predetermined level we present a method to obtain the number of chamber operations to perform the preventive maintenance of that chamber. In addition, a method to get the preventive maintenance schedule for the whole cluster tool is presented. A numerical example is provided to illustrate our method.

• Journal of Korean Institute of Industrial Engineers
• /
• v.1 no.1
• /
• pp.93-98
• /
• 1975

This paper presents a generalized model for determining minimum cost preventive maintenance schedules where accurate failure data are not available except the "average"(mean) and the "typical" value(mode) of the component lifetime.

• Proceedings of the KSR Conference
• /
• 2008.06a
• /
• pp.1359-1370
• /
• 2008

In the track geometry such as rails, sleepers, ballasts and fastener, track deterioration occurs by repetitive train weight and the high-speed railway takes a trend faster than normal. Track deterioration of over threshold value harms ride comfort and furthermore affect in trains safety seriously. An organic and systematic track maintenance system is very important because a trend of the track deterioration effects on track life-cycle and running safety. Also costs of the railway track permanent way and its maintenance are extremely large, forming a significant part of the total infrastructure expenditure. Therefor reasonable and efficient track maintenance has to be planed on a budget. It is required to carry out not only corrective maintenance but preventive maintenance for the track maintenance. In order to perform maintenance jobs in the boundary of the machines and resources given regarding the type and amount jobs, it is necessary to determine feasible or optimal scheduling considering the priority. In this study, the system organization and required functions for the development of track maintenance system supported track deterioration prediction and optimal scheduling are proposed.

• IE interfaces
• /
• v.13 no.4
• /
• pp.680-687
• /
• 2000

The objectives of this research are to establish the operation scheduling and the preventive maintenance system in order to optimize the operation of mailing center. For the optimal operation scheduling of mailing process, the existing workflow of mailing process and that of required time are investigated prior to simulation modeling. The simulation experiments are conducted to increase the nextday delivery rate. The best alternative whose nextday delivery rate up to 100% is selected based the AHP(Analytic Hierarchy Process) method. The optimal work scheduling of all mailing centers are also presented. In addition, the CMMS(Computerized Maintenance Management System) for preventive maintenance is introduced for efficient operation of highly automated facilities of mailing center.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.2
• /
• pp.248-253
• /
• 2007

Traditional time based preventive maintenance is used to constant maintenance interval for equipment life. In order to consider economic aspect for time based preventive maintenance, preventive maintenance is scheduled by RCM(Reliability-Centered Maintenance) evaluation. So, Markov state model is utilized considering stochastic state in RCM. In this paper, a Markov state model which can be used for scheduling and optimization of maintenance is presented. The deterioration process of system condition is modeled by a Markov model. In case study, simulation results about RCM are used to the real historical data of combustion turbine generating units in Korean power systems.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2007.05a
• /
• pp.328-333
• /
• 2007

Traditional maintenance planning is based on a constant maintenance interval for equipment life. In order to consider economic aspect for tm based preventive maintenance, preventive maintenance is desirable to be scheduled by RCM(Reliability-Centered Maintenance) evaluation. The main objective of RCM is to reduce the maintenance cost, by focusing on the most important functions of the system and avoiding or removing maintenance actions that are not strictly necessary. So, Markov state model is utilized considering stochastic state in RCM In this paper, a Markov state model much can be used for scheduling and optimization of maintenance is presented. The deterioration process of system condition is modeled by the stepwise Markov model in detail. Also, because the system is not continuously monitored, the inspection is considered. In case study, simulation results about RCM will be shown using the real historical data of combustion turbine generating unit in Korean power systems.

• Proceedings of the KIEE Conference
• /
• 2006.11a
• /
• pp.357-359
• /
• 2006

Traditional time based preventive maintenance is used to constant maintenance interval for equipment life. In order to consider economic aspect for time based preventive maintenance, preventive maintenance is scheduled by RCM(Reliability-Centered Maintenance) evaluation. So, Markov state model is utilized considering stochastic state in RCM. In this paper, a Markov state model which can be used for scheduling and optimization of maintenance is presented. The deterioration process of system condition is modeled by a Markov model. In case study, simulation results about RCM are used to the real historical data of combustion turbine generating units in Korean power systems.

• Korean Management Science Review
• /
• v.13 no.3
• /
• pp.163-172
• /
• 1996

According to flying hours, aircraft engines require regular overhaul for preventive maintenance. Because of hostile defense environment of Republic of Korea, the aircraft of republic of Korea Air Force(ROKAF) have been operated at the maximum level of availability and have similar overhaul schedule in several months. The concentration of overhaul schedule in a short period demands additional spare engines far exceeding the spare engines for corrective maintenance. If ROKAF decides to purchase extra engines for the preventive maintenance, the extra engines will be used only for the preventive maintenance and will be excess inventory for the most of aircraft life ccle. Also, the procurement of extra engines is significant investment for ROKAF. To help ROKAF schedule the preventive maintenance without significant spending, this study develops a dynamic programming model that is solvable using an integer programming algorithm. The model provides the number of engines that should be overhauled for a month for multiple periods under given constraints. ROKAF actually used this model to solve a T-59 engine overhaul problem and saved about three billion won at one time. ROKAF plans to use this model continuously for T-59 and other weapon systems. Thus, saving for long term will be significant to ROKAF. Finally, with minor modification, this model can be applied to deciding the minimum number of spare engines for preventive maintenance.