Feature selection for bearing fault detection based on mutual information

Karthik Kappaganthu, C. Nataraj, Biswanath Samanta

Research output: Contribution to book or proceedingConference articlepeer-review

4 Scopus citations

Abstract

This paper deals with the important task of feature selection for the detection of faulty bearings in a rotor-bearing system. Various time, frequency and time-frequency based features are obtained from signals measured from bearings with and without outer race defect. The features are divided into a training set, a validation set and a test set. The task is to develop an optimal subset of features for a pattern classification algorithm which can efficiently and accurately classify the state of the machine as healthy or faulty. The features are ranked based on the mutual information content between the feature subset and the state of the machine. A validation set from the measured data is then used to obtain the optimal subset for classification. The performance of the method is evaluated using the test set.

Original languageEnglish
Title of host publicationIUTAM Symposium on Emerging Trends in Rotor Dynamics - Proceedings of the IUTAM Symposium on Emerging Trends in Rotor Dynamics
Editors[email] [email protected] Gupta
PublisherSpringer Verlag
Pages523-533
Number of pages11
ISBN (Print)9789400700192
DOIs
StatePublished - 2011
EventIUTAM Symposium on Emerging Trends in Rotor Dynamics, 2009 - New Delhi, India
Duration: Mar 23 2009Mar 26 2009

Publication series

NameIUTAM Bookseries
Volume25
ISSN (Print)1875-3507
ISSN (Electronic)1875-3493

Conference

ConferenceIUTAM Symposium on Emerging Trends in Rotor Dynamics, 2009
Country/TerritoryIndia
CityNew Delhi
Period03/23/0903/26/09

Scopus Subject Areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Aerospace Engineering
  • Acoustics and Ultrasonics
  • Mechanical Engineering

Keywords

  • Bearing defect
  • Feature selection
  • Mutual information
  • Optimal feature set

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