• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.29-39
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• 2012

The main objective is to predict the future degradation and maintenance budget for a suspension bridge system. Bayesian inference is applied to find the posterior probability density function of the source parameters (damage indices and serviceability), given ten years of maintenance data. The posterior distribution of the parameters is sampled using a Markov chain Monte Carlo method. The simulated risk prediction for decreased serviceability conditions are posterior distributions based on prior distribution and likelihood of data updated from annual maintenance tasks. Compared with conventional linear prediction model, the proposed quadratic model provides highly improved convergence and closeness to measured data in terms of serviceability, risky factors, and maintenance budget for bridge components, which allows forecasting a future performance and financial management of complex infrastructures based on the proposed quadratic stochastic regression model.

• Journal of the Korean Society of Propulsion Engineers
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• v.21 no.3
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• pp.91-100
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• 2017

The solution was developed for the maintenance decision support of combined cycle power plant gas turbine. The developed solution was applied to MHI501G gas turbine and is, in present, on the process of field test at GUNSAN combined cycle power plant, South Korea. The developed solution provides the calculated result of optimal overhaul maintenance period through following modules: Real Time Performance Monitoring, Model-Based Diagnostics, Performance Trend Analysis, Optimal Overhaul Maintenance Interval, Compressor Washing Period Management, and Blade Path Temperature Analysis. Model-Based Diagnostics module analyzed the differences between the data of gas turbine performance model and the online measurement. Compressor washing management module suggests the optimal point of balancing between the compressor performance and the maintenance cost.

• Journal of the Korea Society for Simulation
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• v.26 no.3
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• pp.23-34
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• 2017

This paper is to study the simulation model of depot maintenance system that analyzes logistics supportability such as component availability and cost of target equipment. A depot maintenance system could repair or maintain multiple components simultaneously. The key performance indicators of this system are component availability, repair cycle time, and maintenance cost. The simulation model is based on the engine maintenance process of army aviation depot. This study combines the NOLH(Nearly Orthogonal Latin Hypercube) experimental design method, to composes 33 scenarios, with a multiple regression analysis to find out major factors that influence on key performance indicators. This study is significant in providing a cost-effectiveness analysis on depot maintenance system that is capable of maintaining multiple components at the same time.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.2
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• pp.123-137
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• 2014

The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.3-10
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• 2022

An estimated investment gap of $176 billion needs to be filled over the next ten years to improve America's inland waterway transportation systems. Many of these infrastructure systems are now beyond their original 50-year design life and are often behind in maintenance due to funding constraints. Therefore, long-term maintenance strategies (i.e., asset management (AM) strategies) are needed to optimize investments across these waterway systems to improve their condition. Two common AM strategies include policy-driven maintenance and performance-driven maintenance. Currently, limited research exists on selecting the optimal AM approach for managing inland waterway transportation assets. Therefore, the goal of this study is to provide a decision model that can be used to select the optimal alternative between the two AM approaches by considering key uncertainties such as asset condition, asset test results, and asset failure. We achieve this goal by addressing the decision problem as a single-criterion problem, which calculates each alternative's expected value and certain equivalence using allocated monetary values to determine the recommended alternative for optimally maintaining navigable waterways. The decision model considers estimated and predicted values based on the current state of the infrastructure. This research concludes that the performance-based approach is the optimal alternative based on the expected value obtained from the analysis. This research sets the stage for further studies on fiscal constraints that will effectively optimize these assets condition.

• 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.

• Journal of the Korean housing association
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• v.22 no.6
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• pp.51-60
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• 2011

The maintenance cost depends on various factors such as building volume, floor area, number of household and so on. The maintenance cost of the apartment housing is affected by the maintenance type, building physical factor, sociogeographic aspects. Among these, the maintenance characteristics is represented and made up by the total floor area and number of household which means main factor to provide the building scale roughly. In this paper, it aimed at modelling the estimation function of the maintenance cost with the total floor area and number of household and analyzing the elasticity of the two factors. Although items of maintenance cost are various in general cost, repair cost and so on, we classified these items into the 5 categories. 5 categories are a general cost, a facility maintenance cost, a utilization cost, insurance and sanitary cost. The estimation function used a power function and it has better goodness-offitness than any other estimation methods in statistics. A power function has a three curve types with concave and convex and linear style to the origin.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.1853-1862
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• 2015

Naval maintenance system carries out repairs of battle ships. Korean Navy has four maintenance stations to maximize the readiness of the battle ships. Since each station can provide different services according to characteristics(specific size of ships, type of maintenances) and the maintenance ability of stations is predetermined, it has been one of complex problems for the Korean Navy to find the optimal resource allocation. We investigate the operation of the stations from the perspective of the human resource allocation which plays crucial role in the performance of the maintenance stations. Using a queueing model and optimization technique, we present a way to derive the optimal personnel allocation which minimize the waiting number of battle ships at each station, leading to the improvement of the military readiness in the Korean Navy.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.68-76
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• 2016

The present study proposes the definition of service life and the end-of-life criterion for bridge maintenance. Bridges begin to deteriorate as soon as they are put into service. Effective bridge maintenance requires sound understanding of the deterioration mechanism as well as the expected service life. In order to determine the expected service life of a bridge for effective bridge maintenance, it is necessary to have a clear definition of service life and end-of-life. However, service life can be viewed from several perspectives based on literature review. The end of a bridge's life can be also defined by more than one perspective or performance measure. This study presents definition of service life which can be used for bridge maintenance and the end-of life criterion using the performance measure such as a damage score. The regression model can predict an average service life of bridges using the proposed end-of-life criterion.

• KNOM Review
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• v.24 no.1
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• pp.13-19
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• 2021

In recent, there is research to maximize production by preventing failures/accidents in advance through fault diagnosis/prediction and factory automation in the industrial field. Cloud technology for accumulating a large amount of data, big data technology for data processing, and Artificial Intelligence(AI) technology for easy data analysis are promising candidate technologies for accomplishing this. Also, recently, due to the development of fault diagnosis/prediction, the equipment maintenance method is also developing from Time Based Maintenance(TBM), being a method of regularly maintaining equipment, to the TBM of combining Condition Based Maintenance(CBM), being a method of maintenance according to the condition of the equipment. For CBM-based maintenance, it is necessary to define and analyze the condition of the facility. Therefore, we propose a machine learning-based system and data model for diagnosing the fault in this paper. And based on this, we will present a case of predicting the fault occurrence in advance.