A Study on the Operating Characteristics of the Aged ELCB according to the Overcurrent 


Vol. 38,  No. 5, pp. 1-7, Oct.  2023
10.14346/JKOSOS.2023.38.5.1


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  Abstract

The steel wheels of urban railway vehicles gather a lot of data through regular measurements during maintenance. However, limited research has been carried out utilizing this data, resulting in difficulties predicting the maintenance period. This paper studied a machine learning model suitable for mileage prediction by studying the characteristics of mileage change according to diameter and flange thickness changes. The results of this study indicate that the larger the diameter, the longer the travel distance, and the longest flange thickness is at 30 mm, which gradually shortened at other times. As a result of research on the machine learning prediction model, it was confirmed that the random forest model is the optimal model with a high coefficient of determination and a low root mean square error.

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  Cite this article

[IEEE Style]

박예진, 강신동, 김재호, "A Study on the Operating Characteristics of the Aged ELCB according to the Overcurrent," Journal of the Korean Society of Safety, vol. 38, no. 5, pp. 1-7, 2023. DOI: 10.14346/JKOSOS.2023.38.5.1.

[ACM Style]

박예진, 강신동, and 김재호. 2023. A Study on the Operating Characteristics of the Aged ELCB according to the Overcurrent. Journal of the Korean Society of Safety, 38, 5, (2023), 1-7. DOI: 10.14346/JKOSOS.2023.38.5.1.

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