Next Article in Journal
Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model
Previous Article in Journal
Analysis of the Moment Method and the Discrete Velocity Method in Modeling Non-Equilibrium Rarefied Gas Flows: A Comparative Study
Previous Article in Special Issue
Simple Degree-of-Freedom Modeling of the Random Fluctuation Arising in Human–Bicycle Balance
Article Menu
Issue 13 (July-1) cover image

Export Article

Open AccessArticle

Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique

Department of Mechanical Engineering, Nihon University, Chiba 275-8575, Japan
Current Address: 1-2-1 Izumi-cho, Narashino-shi, Chiba, Japan.
Appl. Sci. 2019, 9(13), 2734; https://doi.org/10.3390/app9132734
Received: 6 June 2019 / Revised: 3 July 2019 / Accepted: 4 July 2019 / Published: 5 July 2019
(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)
  |  
PDF [2002 KB, uploaded 8 July 2019]
  |     |  

Abstract

A track condition monitoring system that uses a compact on-board sensing device has been developed and applied for track condition monitoring of regional railway lines in Japan. Monitoring examples show that the system is effective for regional railway operators. A classifier for track faults has been developed to detect track fault automatically. Simulation studies using SIMPACK and field tests were carried out to detect and isolate the track faults from car-body vibration. The results show that the feature of track faults is extracted from car-body vibration and classified from proposed feature space using machine learning techniques. View Full-Text
Keywords: railway; condition monitoring; fault detection; preventive maintenance; machine learning railway; condition monitoring; fault detection; preventive maintenance; machine learning
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

Tsunashima, H. Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique. Appl. Sci. 2019, 9, 2734.

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]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top