• Journal of the Korean Society for Railway
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• v.16 no.3
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• pp.183-188
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• 2013

The purpose of maintaining rolling stock is to perform train service without failure during operation. It is not possible to prevent failure, however, by using periodic preventive maintenance methods, because new rolling stock is made from many electric components and requires the application of IT skills. RAMS (Reliability, Availability, Maintainability, Safety) methods have consequently been applied to new manufactured rolling stock in KORAIL since implementation of the KTX. With this approach it is possible to verify the reliability at the operating stage, and RCM (Reliability Centered Maintenance) methods for maintenance have been applied to manufactured rolling stock since the beginning of KTX service. A RCRM (Reliability Centered Rolling-stocks Maintenance) system suitable for the characteristics of rolling stock and operational factors is introduced in this paper.

• Journal of the Korean Society for Railway
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• v.9 no.5 s.36
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• pp.606-611
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• 2006

This paper presents a procedure and an analysis method to evaluate reliability of the Korea high-speed train. The rolling stock system is divided into 6 sub-systems and each subsystem is classified into sub-assemblies. Functional analysis has been conducted to draw reliability block diagrams for the sub-systems. First, failure rates has been calculated for each sub-assembly from the failure data obtained during commissioning tests. Then a reliability block diagram is used to evaluate the MKBF(Mean Kilometers Before Failure) of the sub-systems. Activities to increase reliability have been carried out throughout the test runs and analysis results show that the reliability of the rolling stock system is gradually growing in time.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.487-497
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• 2008

This paper addresses the Fault Monitoring System developed by Hyundai-Rotem based on the Mil-HDBK-2155 Failure Reporting, Analysis and Corrective Action System. The purpose of a FRACAS is to provide a documented history of any problems, failures, their associated corrective actions as well as detailing how and why each problem arose or failure occurred during the Warranty Period for Rolling Stock delivered by Hyundai-Rotem. The Fault Monitoring System can assess the reliability, availability and maintainability as well as can predict no. of failure at a time which can input to Life Cycle Cost.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.4
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• pp.220-230
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• 2015

Rolling stock RAMS is a field of engineering which integrates reliability, availability, maintainability and safety (RAMS) characteristics into an inherent product design property through rolling stock system engineering process. It is implemented to achieve operational objectives successfully, and recently the RAMS has become a rapidly growing engineering discipline because it has a great potential to ensure safety and improve cost effectiveness. However, the Korean rolling stock industry has not yet implemented RAMS management in the rolling stock engineering process, despite the issue having been addressed since the introduction of the KTX. Thus, this paper discusses the processes, methods and techniques for RAMS assessment in three parts. Firstly, it outlines a process of the overall RAMS performance assessment for achieving technical RAMS design criteria. Secondly, it discusses a process for assessing the operational RAM and allocating the RAM. This paper also proposes a model for assessing safety-based risk management, which includes five analytic techniques for identifying the causes and consequences of a system failure. Finally, a case example is provided for the risk assessment of the pneumatic braking device.

• International Journal of Railway
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• v.1 no.3
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• pp.89-93
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• 2008

Reliability of onboard power supply systems on rolling stock is a very important issue for the railway operator. While a failure of the HVAC supply results in a loss of comfort for the passengers, a failure of the supply for air compressors or for the traction cooling systems results in towing the train. This is, looking at the required availability of a train, not acceptable. An active redundancy concept for the onboard power supply maximizes the availability of the system. This paper describes such a system under the aspect of $\cdot$ Weight reduction $\cdot$ Continuous operation when changing from normal to redundant operation $\cdot$ Flexibility in train design.

• Proceedings of the KSR Conference
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• 2005.11a
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• pp.1083-1088
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• 2005

As the industrial society develops rapidly, the weight reduction and high strength are demanded gradually. In case of the welded joint for the rolling stock which receives the repeat load, the structural failure can occur easily. However, if the shot peening technique is applied, the durability and the fatigue characteristics of the rolling stock will be improved because the hardness increases and the residual stress is induced. In this study, the fatigue characteristics of shot peened welled joints for the rolling stock was investigated. The crack initiation was examined and hardness was also evaluated. the results show that the fatigue characteristics of welded joints was improved.

• Journal of Information Technology Applications and Management
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• v.29 no.6
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• pp.63-80
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• 2022

In this paper, we study structural changes and performance gains in condition-based maintenance process redesign when mobile IoT technology is embedded into urban railway rolling stock. We first develop condition-based maintenance To-Be process model in accordance with the IoT deployment scheme. Secondly, we draw upon theoretical framework of redesign process analysis to develop performance evaluation method suitable to predictive maintenance shift from As-Is ordinary maintenance practice. Subsequently, To-Be process performance evaluations are conducted adopting both the quantitative and qualitative method for time, cost, and dependability dimensions. The results ascertain the considerable benefits captured through detection abnormality prior to actual rolling stock failure occurrence, and details of performance improvements and enhancement of standardization level is revealed. The procedures and results presented in this paper offers useful insights in the fields of IoT economic analysis, condition based maintenance, and business process redesign.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• 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.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1486-1493
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• 2008

In this paper, we performed analysis and comparison on emergency response action for passengers, drivers, workers and controllers in case of failure occurrence on rail infrastructure and rolling stock. In general, the subjects of emergency response action perform the response action with following emergency response procedures when accidents occurred. In reality, however, no matter how well the subjects are trained, it is hard to follow the emergency response procedures precisely without making any mistakes. As for emergency response action, the most significant factor for the subjects is to follow the emergency response procedures as learned, without any hesitation. In this paper, therefore, we analyzed the emergency response actions that should be performed by passengers and railway workers when emergency accidents occurred. We also examined the communication facilities for emergency response among train, wayside and station in order to provide the emergency reporting system for passenger and the method for cleaning out the accident area.