Journal
ISA TRANSACTIONS
Volume 52, Issue 2, Pages 278-284Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2012.12.006
Keywords
Vibration fault diagnosis; Teager-Kaiser energy operator; Feature selection; Neural networks; LS-SVM
Categories
Funding
- Spanish government MCINN [TEC2009-14123-004]
- ACIISI of the Canary Autonomous Government (Spain)
- European Social Fund (ESF)
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Condition monitoring of rotating machines is important in the prevention of failures. As most machine malfunctions are related to bearing failures, several bearing diagnosis techniques have been developed. Some of them feature the bearing vibration signal with statistical measures and others extract the bearing fault characteristic frequency from the AM component of the vibration signal. In this paper, we propose to transform the vibration signal to the Teager-Kaiser domain and feature it with statistical and energy-based measures. A bearing database with normal and faulty bearings is used. The diagnosis is performed with two classifiers: a neural network classifier and a LS-SVM classifier. Experiments show that the Teager domain features outperform those based on the temporal or AM signal. (C) 2012 ISA. Published by Elsevier Ltd. All rights reserved.
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