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Health Monitoring of Gear Elements Based on Time-Frequency Vibration by Support Vector Machine Algorithms

[+] Author Affiliations
D. J. Bordoloi, Rajiv Tiwari

Indian Institute of Technology Guwahati, Guwahati, India

Paper No. GTINDIA2013-3772, pp. V001T05A019; 11 pages
  • ASME 2013 Gas Turbine India Conference
  • ASME 2013 Gas Turbine India Conference
  • Bangalore, Karnataka, India, December 5–6, 2013
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5616-1
  • Copyright © 2013 by ASME


Health monitoring of a gear box has been attempted by the support vector machine (SVM) learning technique with the help of time-frequency (wavelet) vibration data. Multi-fault classification capability of the SVM is suitably demonstrated that is based on the selection of SVM parameters. Different optimization methods (i.e., the grid-search method (GSM), the genetic algorithm (GA) and the artificial bee colony algorithm (ABCA)) have been performed for optimizing the SVM parameters. Four fault conditions have been considered including the no defect case. Time domain vibration signals were obtained from the gearbox casing operated in a suitable speed range. The continuous wavelet transform (CWT) and wavelet packet transform (WPT) are extracted from time domain signals. A set of statistical features are extracted from the wavelet transform. The classification ability is noted and compared against predictions when purely time domain data is used, and it shows an excellent prediction performance.

Copyright © 2013 by ASME



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