Traction Diesel Engine Anomaly Detection Using Vibration Analysis in Octave Bands

Traction machines are essential parts for a train to run. Therefore, a condition monitoring system (CMS) is being developed, that detects machine failure in the early stages to prevent traffic disruption. The CMS observes the vibrations of a machine and detects abnormal vibrations with a machine learning algorithm. In the CMS, octave-band analysis is performed to extract feature vectors from vibration data. Running tests were conducted to verify the performance of the CMS. Test results showed that simulated abnormal vibrations were clearly distinguishable from normal ones with the CMS.

• Record URL:
  https://doi.org/10.2219/rtriqr.57.2_105
• Summary URL:
  http://scholar.google.com/scholar_lookup?title=Traction+Diesel+Engine+Anomaly+Detection+Using+Vibration+Analysis+in+Octave+Bands&author=M.+KONDO&author=S.+MANABE&author=T.+TAKASHIGE&author=H.+KANNO&publication_year=2016
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00339008
• Authors:
  • KONDO, Minoru
  • MANABE, Shinichi
  • TAKASHIGE, Tatsuro
  • KANNO, Hiroshi
• Publication Date: 2016

Language

• English
• Japanese

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 105-111
• Serial:
  • Quarterly Report of RTRI
  • Volume: 57
  • Issue Number: 2
  • Publisher: Railway Technical Research Institute
  • ISSN: 0033-9008
  • EISSN: 1880-1765
  • Serial URL: https://www.jstage.jst.go.jp/browse/rtriqr/-char/en
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Diesel engines; Railroad trains; Traction; Vibration
• Uncontrolled Terms: Anomalies; Frequency bands
• Subject Areas: Maintenance and Preservation; Railroads; Vehicles and Equipment;

Filing Info

• Accession Number: 01602172
• Record Type: Publication
• Source Agency: Japan Science and Technology Agency (JST)
• Files: TRIS, JSTAGE
• Created Date: Jun 20 2016 10:27AM