• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.303-312
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• 2018

The university facility is made up of multiple buildings and has many maintenance items. In addition, administrative constraints need to be handled within a limited period. Most maintenance work is small scale and multi-work construction, such as waterproofing, needs to be organized. The facility manager makes annual unit price contract with a maintenance company and carries out the maintenance work. On the other hand, delay and rework is occurring because existing maintenance work performed without scheduling based on the manpower input. This study proposed a scheduling model that can support the facility manager to manage maintenance works of multiple university facilities at the program level. The model consists of three stages in order. In object analysis, details of the maintenance items were analyzed and the quantity is calculated based on the quantity takeoff sheet. In resource analysis, the craftsmen and construction period of detailed works are derived for the effective input of craftsmen. In scheduling, the priority of each work and the optimal manpower input are derived. The optimal schedule is selected according to the goodness of fit. The applicability and effectiveness of the prototype was evaluated through a case study and interviews with case participants. The model was found to be an effective tool to support the scheduling of maintenance works for the facility manager.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.909-923
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• 2008

This paper considers the replacement model and the preventive maintenance model following the expiration of combination warranty for a repairable system. If the system fails after the combination warranty is expired, then it is minimally repaired at each failure. The criterion used to determine the optimal replacement policy and the optimal preventive maintenance policy is the overall value function based on the expected cost rate per unit time and the expected downtime per unit time. The numerical examples are presented for illustrative purpose when the failure time follows a Weibull distribution.

• Journal of the military operations research society of Korea
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• v.30 no.2
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• pp.96-107
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• 2004

This paper deals with $\ulcorner$Requirement Decision Model for Repair Parts supplied by the Government$\lrcorner$ which is to reduce Aircraft Contract Maintenance Cost. It aims to find solutions to the fundamental problems of the Aircraft Contract Maintenance System. Under the current Aircraft Contract Maintenance System, it is hard to forecast the exact demand of repair parts, so support rate of Repair Parts supplied by the Government is restricted under 50 percent. It is inevitable to purchase Repair Parts from the firm with much higher price than those of Government source. However, absence of fixed demand pattern makes it difficult to improve accuracy of demand forecast. As a solution to these problems, this model prevents a cost increase due to the unit price difference between Repair Parts supplied by the Government and Repair Parts purchased by the Firm. It also reflects demand characteristics of each repair part, and prevents continual stock increase by setting an upper limit on the amount of Repair Parts supplied by the Government. The effectiveness of this model is verified by empirical analysis using the latest raw data. By applying this model to real situation, we expect to reduce about 4 billion won every year.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.168-175
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• 2023

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.130-137
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• 2017

To maintain traffic safety during the target lifetime of bridges, it is essential to secure an appropriate maintenance budget and allocate that budget appropriately. This paper proposes a reasonable budget allocation system that considers various impact factors to improve the conventional budget allocation method simply considering the bridge scale. The maintenance action rate model and the unit cost model based on the prior maintenance history were developed to allocate appropriately the bridge maintenance budget for the total bridges of the management organization with the target management level. A method to determine the optimal budget allocation ratio for each management subject was proposed and case analysis was conducted using the proposed model. Proper budget allocation was made considering the bridge types, current safety level, and service life as well as the bridge size as an impact factor of the budget allocation of the bridge. The developed method can prevent budget waste and provide a rational basis for budget allocation by implementing the rational budget distribution.

• Journal of the Korea Safety Management & Science
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• v.25 no.4
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• pp.141-151
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• 2023

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.1
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• pp.64-75
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• 2013

As both laboratory accelerated experiment and field exposure experiment were performed, at recent, the fifth field test at five year exposures was proceeded according to long period experimental plan. Field experiment, for the adoption of the developed evaluation model, which is consisted of the analysis of chloride penetration profile at gate bridges of sea-dike completed 30 years ago was carried out during upgrading the basic evaluation model with analyzing the annual field test data. The surface concentration of chlorides was replaced to the concentration of chloride of inner concrete near the surface chlorides among his research results at basic model. Maage's suggestion function was accepted too as a diffusion coefficient of chloride after verifying the change of diffusion coefficient by analysis of annual field test data. The comparison of field data with model predictions and the estimation of remaining life time demonstrates that the proposed updated model and maintenance simulation system can be used to predict the chloride penetration profile in the marine tidal zone and appropriate repair period and cost.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.297-304
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• 2021

The maintenance management system of domestic public facilities is shifting from the concept of diagnosis and repair for maintaining safety of facilities to performance evaluation method that improves and maintains the performance of facilities. However, in Korea, the performance evaluation method for the maintenance of facilities has not been established, so the performance evaluation has not been properly performed. In this study, we propose an asset management model that introduces the concept of performance evaluation to complement the simple maintenance strategy focused on safety management of current facilities. This model differentiated from the existing model by proposing a method for using WBS as a method for collecting facility maintenance information. This model proposed the most important level of service evaluation method and performance measure factors in the performance evaluation of facilities, and conducted a case analysis to assess the appropriateness of this model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.331-336
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• 2018

In this paper, the life cycle cost of the auxiliary power unit in the conventional 8200 series electric locomotive is evaluated and an effective life cycle cost reduction method is sought. For this, a life cycle cost evaluation model was proposed using IEC 60300-3-3 standard. As a result of analysis, material cost which accounted for a large percentage of preventive maintenance cost, accounted for 64% of total cost, and breakdown maintenance cost was as high as 27%. Except for the cost of preventive maintenance, the breakdown maintenance cost ratio was the highest. In order to reduce the LCC of the auxiliary power unit(APU) of the 8200 series in the future, it is necessary to reduce the material cost in case of development and to secure the high reliability according to the parts manufacturing so as to minimize the maintenance cost.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.338-346
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• 2016

In order for the optimal solution of generators’ annual maintenance scheduling to be applicable to the actual power system it is crucial to incorporate the constraints related to the equivalent operation hours (EOHs) in the optimization model. However, most of the existing researches on the optimal maintenance scheduling are based on the assumption that the maintenances are to be performed periodically regardless of the operation hours. It is mainly because the computation time to calculate EOHs increases exponentially as the number of generators becomes larger. In this paper an efficient algorithm based on demand grouping method is proposed to calculate the approximate EOHs in an acceptable computation time. The method to calculate the approximate EOHs is incorporated into the optimization model for the maintenance scheduling with consideration on the EOHs of generators. The proposed method is successfully applied to the actual Korean power system and shows significant improvement when compared to the result of the maintenance scheduling algorithm without consideration on EOHs.