• Journal of Korean Society for Quality Management
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• v.29 no.2
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• pp.46-53
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• 2001

In this paper, a preventive maintenance(PM) policy for a repairable system is considered. The failure rate model proposed by Park et at.(2000) is generalized by assuming that after each PM not only the PM slows down the degradation process of the system but also reduces down the system failure rate by a certain fixed amount. Long-run expected cost rate of the PM policy is derived and the properties of joint solution of the optimal PM period and optimal number of PM which minimizes the expected cost rate are obtained. Numerical examples for the case of a Weibull-type failure rate are given.

• International Journal of Reliability and Applications
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• v.3 no.3
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• pp.113-124
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• 2002

This paper suggests the optimal periodic preventive maintenance policies after the combination warranty is expired. After the combination warranty is expired, a repairable system undergoes PM periodically and is minimally repaired at each failure. And also the system is replaced by a new system at the N th PM. In this case, we derive the mathematical formula for the expected cost rate per unit time. The optimal number and period for the periodic PM that minimize the expected cost rate per unit time are obtained. Some numerical examples are presented for illustrate purpose.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.999-1007
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• 2009

This paper considers the optimal periodic preventive maintenance (PM) policy following the expiration of free-repair warranty. We assume that two periodic PM models with random maintenance quality which were proposed by Wu and Clements-Croome (2005) and Jung (2006b), respectively. Given the cost structure to the user during the cycle of the product, we derive the expressions for the expected cost rate per unit time. Also, we obtain the optimal PM number and the optimal PM period by minimizing the expected cost rate per unit time. The numerical examples are presented for illustrative purpose.

• Plant Journal
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• v.12 no.2
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• pp.31-35
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• 2016

A method of failure data management for Reliability-Centered Maintenance was shown for a boiler feedwater pump of a power plant. The major part of it is an analysis of failure mode, failure cause, and failure effects, which is the main component of a failure data base like OREDA(Offshore Reliability Data). Case study shows main element of the preventive maintenance planning such as the maintenance period can be statistically determined from the failure data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.12
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• pp.1482-1487
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• 2009

In this study statistical analysis was performed for NDI(Non-Destructive Inspection) results of F100 engine front seal support assembly. NDI results can be statistically considered as Quantal Response Data. It is found that the suitable probability distribution to the failure data is normal distribution through MLE(Maximum Likelihood Estimation) of the Quantal Response Data. Moreover, Cumulative Distribution Function, failure rate function and B-Life are calculated on the supposed distribution.

• Tribology and Lubricants
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• v.10 no.4
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• pp.51-58
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• 1994

Condition monitoring technology has recently been received much attention in the light of its significance on the maintenance of complex machineries such as turbines in power plants. Currently, turbines in power plants are maintained by scheduled overhaul based on the manufacturer's recommendations and the utility's experience. Although this preventive maintenance is known to be very effective, operators have less access to identify failure of elements when it happens between overhaul period. Therefore, in this study, a development of a on-line condition monitoring system through wear debris analysis of lubricating oils is aimed with a view to detecting abnormal wear behaviour of bearings and other wet-components at an early stage, allowing better outage scheduling and minimizing forced outages. For field application purposes, the on-line system developed was installed on the turbine of the No.4 unit at Ulsan Power Plant and its performance has been evaluated on site.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.791-798
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• 2014

Wu and Clements-Croome (2005) suggest the preventive maintenance (PM) model with random maintenance quality. They assume that each PM resets the failure rate to zero and the rate of increases of the failure rate gets higher after each additional PM. However a system may not be restored to as good as new immediately after the completion of PM. Thus, this paper modifies the Wu and Clements-Croome's PM model and then the optimal PM policy is suggested. To determine the optimal PM policy, we utilize the expected cost rate per unit time for our model. That is, we obtain the optimal number and the optimal period by minimizing the expected cost rate per unit time. The numerical examples are presented for illustrative purpose.

• Journal of Navigation and Port Research
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• v.30 no.6 s.112
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• pp.525-531
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• 2006

In the port, the yard crane is very important. If a container crane or a transfer crane is broken down, it costs a lot because of the delay of work during the period of repair or reorder. But, we don't have enough spare parts because of the high cost. It is necessary to maintain high reliability of the crane through effective preventive maintenance and failure analysis. In this paper, we analyse the function and failure mechanism of the transfer crane which is a main equipment in the yard Also, we standardize failures and maintenance works using the historical data of failure and maintenance. This study which is a basic work for effective equipment operation and maintenance will support reliability engineers to decide the optimal design of the next generation equipment and operational policy of equipment.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.06b
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• pp.363-366
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• 2006

In the port, Yard Crane is very important. If container crane or transfer crane broke down, it costs much money for delaying of work during the period of repair or reorder. But, we can not have enough spare parts for its high cost. It is necessary for having the reliability of crane through the effective preventive maintenance and failure analysis. In this paper, we analysed the system's function and failure mechanism of transfer crane which is main equipment in the yard. Also, we standardized the failure and maintenance work using the historical data of failure and maintenance. This study which is the basic work for IT of equipment operation and maintenance is going to be new attempt for optimal design of next generation equipment and operation policy of equipment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.