Fault diagnosis of railway point machines using dynamic time warping
- Authors
- Kim, H.; Sa, J.; Chung, Y.; Park, D.; Yoon, S.
- Issue Date
- 12-5월-2016
- Publisher
- INST ENGINEERING TECHNOLOGY-IET
- Keywords
- fault diagnosis; condition monitoring; railway electrification; fault diagnosis; railway point machines; dynamic time warping; condition monitoring method; in-field failure data; railway train operations; RPM movement; abnormal electric-current shape detection; training-based methods
- Citation
- ELECTRONICS LETTERS, v.52, no.10, pp.818 - 819
- Indexed
- SCIE
SCOPUS
- Journal Title
- ELECTRONICS LETTERS
- Volume
- 52
- Number
- 10
- Start Page
- 818
- End Page
- 819
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/88657
- DOI
- 10.1049/el.2016.0206
- ISSN
- 0013-5194
- Abstract
- A practical condition monitoring method is proposed for the fault diagnosis of railway point machines (RPMs) by considering the difficulty of obtaining in-field failure data. Failures in RPMs have a significant effect on railway train operations, and it is very crucial to detect abnormal conditions in RPMs. However, it is generally difficult to obtain in-field failure data for a classifier training step. A diagnosis method using dynamic time warping is proposed to manage the variation in durations of RPM movement without a training step. On the basis of the experimental results with RPMs operated in Korea, it is believed that the proposed method without a training step can detect abnormal electric-current shapes more accurately than previous training-based methods.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Graduate School > Department of Computer and Information Science > 1. Journal Articles
- College of Science and Technology > Department of Computer Convergence Software > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.