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A New Method for Feature Selection and Gear Defect Detection

[+] Author Affiliations
Yan Chen, Haixia Wang, Jay Lee

University of Cincinnati, Cincinnati, OH

Paper No. MSEC2007-31091, pp. 483-487; 5 pages
doi:10.1115/MSEC2007-31091
From:
  • ASME 2007 International Manufacturing Science and Engineering Conference
  • ASME 2007 International Manufacturing Science and Engineering Conference
  • Atlanta, Georgia, USA, October 15–18, 2007
  • Conference Sponsors: Manufacturing Division
  • ISBN: 0-7918-4290-8 | eISBN: 0-7918-3809-9
  • Copyright © 2007 by ASME

abstract

This paper presents a new method for gear defect detection. Firstly, the feature subset that describes a gear health state is generated, followed by two sub-steps: (I) features that are robust to data noise are extracted from the collected data utilizing the information-theoretical concept of entropy; (II) the optimal feature subset for gear defect detection is selected using a wrapper approach. Secondly, patterns of the feature subsets that describe the most current gear health states are grouped into a health-map, using the self-organizing map (SOM) method. This health-map shows a clear clustering of healthy and faulty gears, and can be used as a reference for future gear health evaluation. An industrial case study is presented that shows the effectiveness of the presented method.

Copyright © 2007 by ASME

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