• Journal of Korea Water Resources Association
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• v.36 no.1
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• pp.45-59
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• 2003

The method in this study, which is more efficiency than the existing method, propose the optimal rehabilitation model based on the deterioration prediction of the laying pipe by using the deterioration survey method of the water distribution system. The deterioration prediction model divides the deterioration degree of each pipe into 5 degree by using the probabilistic neural network. Also, the optimal residual durability is estimated by the calculated deterioration degree in each pipe and pipe diameter. The optimal rehabilitation model by integer programming base on the shortest path can calculate a time and cost of maintenance, rehabilitation, and replacement. Also, the model is divided into budget constraint and no budget constraint. Consequently, the model proposed by the study can be utilized as the quantitative method for the management of the water distribution system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.881-885
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• 2013

In this paper, we present a deterioration judgment model of urban rail power equipment using driving history, the frequency and number of failures. In addition, we have developed a deterioration judgment program based on the derived failure rate. A deterioration judgment model of power equipments on metro system was designed to establish how much environmental factors, such as thermal cycling, humidity, overvoltage and partial discharge. The deterioration rate of the transformers followed the Arrhenius log life versus reciprocal Kelvin temperature (hotspot temperature) relation. The deterioration judgment program is linked to the online condition monitoring system of urban railway system. The deterioration judgment program is based on the user interface it is possible to apply immediately to the urban rail power equipment.

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

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Journal of Korean Society for Quality Management
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• v.35 no.4
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• pp.61-66
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• 2007

The performance of a building material degrades as time goes by and the failure of the material is often defined as the point at which the performance of the material reaches a pre-specified degraded level. Based on a stochastic deterioration model, a performance based service life prediction method for building materials and components is developed. As a stochastic degradation model, a gamma process is considered and lifetime distribution and service life of a material are predicted using the degradation model. A numerical example is provided to illustrate the use of the proposed service life prediction method.

• Journal of the Korean Society for Railway
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• v.8 no.3
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• pp.260-266
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• 2005

The deterioration of a shunting locomotive was characterized for the lifetime assessment. The locomotive has been used for shunting works in steel making processes, and in this investigation, various types of technical evaluation methods for the locomotive parts were employed to assess the current deterioration status and to provide important clue for lifetime prediction. Unlike other rolling stocks in railway applications, the diesel shunting locomotive is composed of major components such as diesel engine, transmission, gear box, brake system, electronic devices, etc., which cover more than 70 percent of the total price of the locomotive. Therefore, in this paper, each part of major components in the diesel locomotive was analyzed in terms of the degree of deterioration. The lift-cycle-cost (LCC) analysis was performed based on the maintenance and repair history as compared with economical cost to provide the cost-effective prediction, i.e., to assess either repair for reuse or putting the locomotive out of service based on cost-effective calculation.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.551-556
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• 2004

The deterioration of a shunting locomotive was characterized for the lifetime assessment. The locomotive has been used for shunting works in steel making processes, and in this investigation, various types of technical evaluation methods for the locomotive parts were employed to assess the current deterioration status and to provide important clue for lifetime prediction. Unlike other rolling stocks in railway applications, the diesel shunting locomotive is composed of major components such as diesel engine, transmission, gear box, brake system, electronic devices, etc., which cover more than 70 percent of the total price of the locomotive. Therefore, in this paper, each part of major components in the diesel locomotive was analyzed in terms of the degree of deterioration. The life-cycle-cost (LCC) analysis was performed based on the maintenance and repair history as compared with economical cost to provide the cost-effective prediction, i.e., to assess either repair for reuse or putting the locomotive out of service based on cost-effective calculation.

• Journal of Korean Society of Steel Construction
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• v.28 no.5
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• pp.325-335
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• 2016

To predict service life of coating systems registered in Korean specifications for steel bridge coatings, field deterioration evaluation and accelerated weatherproof test were carried out, and deterioration models were drawn through regression analysis for evaluation results. For the coating systems that have not been used in field, regression analyses were carried out for the virtual evaluation results drawn by applying coordination factor to the field evaluation results for chlorinated rubber and urethane topcoat system. Service life prediction results showed that application of thermal sprayed coating (TSC) could extend service life of coatings to more than twice of general coatings.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.11a
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• pp.531-534
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• 2005

The basic prediction model was constructed to obtain optimal maintenance method for concrete structure under marine environment by exploring the mechanism of mono and combined deterioration in lab. This model was planned to be upgraded with data acquired from several exposure specimens under same environment as structures. The computer program developed to give useful guidance observer would be improved. Several repair materials and repair construction methods applied to exposure specimens will be tested for its performance of prohibit salt attack and freezing & thawing action during experimental period about ten years. All of these data could be available to complete the prediction system. The manager will be able to use the system for optimal maintenance of marine concrete structures.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.