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Open AccessArticle

Aging Detection of Electrical Point Machines Based on Support Vector Data Description

Department of Computer Convergence Software, Korea University, Sejong Campus, Sejong City 30019, Korea
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Symmetry 2017, 9(12), 290; https://doi.org/10.3390/sym9120290
Received: 28 September 2017 / Revised: 16 November 2017 / Accepted: 22 November 2017 / Published: 24 November 2017
Electrical point machines (EPM) must be replaced at an appropriate time to prevent the occurrence of operational safety or stability problems in trains resulting from aging or budget constraints. However, it is difficult to replace EPMs effectively because the aging conditions of EPMs depend on the operating environments, and thus, a guideline is typically not be suitable for replacing EPMs at the most timely moment. In this study, we propose a method of classification for the detection of an aging effect to facilitate the timely replacement of EPMs. We employ support vector data description to segregate data of “aged” and “not-yet-aged” equipment by analyzing the subtle differences in normalized electrical signals resulting from aging. Based on the before and after-replacement data that was obtained from experimental studies that were conducted on EPMs, we confirmed that the proposed method was capable of classifying machines based on exhibited aging effects with adequate accuracy. View Full-Text
Keywords: aging effect diagnosis; electrical point machines; electric current signal processing aging effect diagnosis; electrical point machines; electric current signal processing
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Sa, J.; Choi, Y.; Chung, Y.; Lee, J.; Park, D. Aging Detection of Electrical Point Machines Based on Support Vector Data Description. Symmetry 2017, 9, 290.

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