• Journal of the Korean Society for Railway
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• v.6 no.4
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• pp.252-256
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• 2003

Smart materials can adapt to changes of environment like living organs in nature such that they can maximize the performance and minimize the maintenance expense of engineering systems. Such materials have been paid attention ten years ago and applied in the area of industry, aerospace, transportation and civil structures. This paper summarizes smart material technology and shows some application examples in railway vehicles. Also, its future of smart material technology in railway vehicle technology is envisaged based on its possibility and practical aspect.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.717-724
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• 2017

To improve the running safety of a tram operating on a concrete embedded track, a bogie, the core system of the tram, was developed and fabricated. After it was integrated with the prototype car body, a short distance track with a sharp curve and steep gradient was constructed for the test operation. A formula to check the interference of the wheel flange with the track during running was proposed. Based on the results provided by the formula, the track was designed. Another simple formula was derived to estimate the derailment quotient and the wheel unloading ratio. During running on the track, the acceleration of the car body was measured and the interface status between the wheel and the track was monitored by a video system. According to the results calculated by these simple formulas, the derailment quotient and wheel unloading ratio were estimated to be within the safety criteria. In the actual test, no derailment occurred and the measured acceleration satisfied the criteria. Also, there was no interference between the wheel and track. The video monitoring results showed no signs of derailment, such as the climbing of the wheel. The pinion in the center showed good running safety, contacting smoothly with the rack. The measurements of environmental noise proved that the criteria were satisfied.

• Journal of the Korean Society for Railway
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• v.16 no.1
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• pp.26-31
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• 2013

The reduction unit is one of the most important components for railway vehicles because the torque of the motor must be transmitted to the wheels of the vehicle by the reduction unit. The faults in the reduction units of high-speed trains are caused by damage such as gear, fatigue. These have serious impacts on safety of the train during operation. To address this development of a system for monitoring, fault diagnosis of the reduction unit is needed to keep the vehicle running safely. Before that can be accomplished, it is most important to understand the vibration characteristics of the reduction unit in a normal state. Vibration diagnosis technology using characteristic-analysis of vibration waveform and frequency is known to be the most effective method for fault diagnosis. In this paper, we analyzed the vibration characteristics of the reduction units two Korean high-speed trains (KTX and KTX II), under normal conditions, by two test methods (driving gear test, full-vehicle test).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.