Fault Diagnosis of High-speed Train Bogie Based on Spectrogram and Multi-channel Voting

Su, L, Ma, L, Qin, N et al. (2 more authors) (2018) Fault Diagnosis of High-speed Train Bogie Based on Spectrogram and Multi-channel Voting. In: Proceedings of DDCLS 2018. 2018 IEEE 7th Data Driven Control and Learning Systems Conference, 25-27 May 2018, Enshi, China. IEEE , pp. 22-26. ISBN 978-1-5386-2618-4

Abstract

Metadata

Authors/Creators:
  • Su, L
  • Ma, L
  • Qin, N
  • Huang, D
  • Kemp, A
Copyright, Publisher and Additional Information: © 2018 IEEE. This is an author produced version of a paper published in 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: High-speed train; Bogie; Fault; Severities; Classification; Spectrogram; Random forest; Voting; Multi-channel
Dates:
  • Published: 1 November 2018
  • Accepted: 1 March 2018
  • Published (online): 1 November 2018
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 06 Feb 2019 15:29
Last Modified: 06 Feb 2019 15:29
Status: Published
Publisher: IEEE
Identification Number: https://doi.org/10.1109/DDCLS.2018.8516061
Related URLs:

Share / Export

Statistics