Next Article in Journal
Multi-Device to Multi-Device (MD2MD) Content-Centric Networking Based on Multi-RAT Device
Previous Article in Journal
A New Hesitant Fuzzy Linguistic TOPSIS Method for Group Multi-Criteria Linguistic Decision Making
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Symmetry 2017, 9(12), 290; https://doi.org/10.3390/sym9120290

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
*
Author to whom correspondence should be addressed.
Received: 28 September 2017 / Revised: 16 November 2017 / Accepted: 22 November 2017 / Published: 24 November 2017
  |  
PDF [2593 KB, uploaded 30 November 2017]
  |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top