<b>Traction Diesel Engine Anomaly Detection Using Vibration Analysis in Octave Bands</b>

• KONDO Minoru

  Drive Systems Laboratory, Vehicle Control Technology Division

• MANABE Shinichi

  Drive Systems Laboratory, Vehicle Control Technology Division

• TAKASHIGE Tatsuro

  Drive Systems Laboratory, Vehicle Control Technology Division

• KANNO Hiroshi

  Drive Systems Laboratory, Vehicle Control Technology Division

抄録

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.

収録刊行物

• Quarterly Report of RTRI

  Quarterly Report of RTRI 57 (2), 105-111, 2016

  公益財団法人 鉄道総合技術研究所

キーワード

• vibration analysis in octave bands
• condition monitoring
• machine learning

詳細情報

• CRID: 1390001204464444416
• NII論文ID: 130005156562
• DOI: 10.2219/rtriqr.57.2_105
• ISSN: 18801765
• ISSN: 00339008
• Web Site: https://www.jstage.jst.go.jp/article/rtriqr/57/2/57_105/_pdf
• 本文言語コード: en
• データソース種別:
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  • Crossref
  • CiNii Articles
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