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Mutual Information Based Feature Selection From Data Driven and Model Based Techniques for Fault Detection in Rolling Element Bearings

[+] Author Affiliations
Karthik Kappaganthu

Cummins, Inc., Columbus, IN

C. Nataraj

Villanova University, Villanova, PA

Paper No. DETC2011-47822, pp. 941-953; 13 pages
doi:10.1115/DETC2011-47822
From:
  • ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 23rd Biennial Conference on Mechanical Vibration and Noise, Parts A and B
  • Washington, DC, USA, August 28–31, 2011
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5478-5
  • Copyright © 2011 by ASME

abstract

This paper proposes a novel technique combining datadriven and model-based techniques to significantly improve the performance in bearing fault diagnostics. Features that provide best classification performance for the given data are selected from a combined set of data driven and model based features. Some of the common data driven techniques from time, frequency and time-frequency domain are considered. For model based feature extraction, recently developed cross-sample entropy is used. The ranking and performance of each of these feature sets are studied, when used independently and when used together. Mutual information based technique is used for ranking and selection of the optimal feature set. Using this method, the contribution to performance and redundancy of each of the data driven features and model based features can be studied. This method can be used to design an effective diagnostic system for bearing fault detection.

Copyright © 2011 by ASME

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