Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/44012
Title: Application of the Teager-Kaiser energy operator in bearing fault diagnosis
Authors: Henríquez Rodríguez, Patricia
Alonso, Jesús B. 
Ferrer, Miguel A. 
Travieso, Carlos M. 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Vibration fault diagnosis, Teager–Kaiser energy operator, Feature selection, Neural networks, LS-SVM
Issue Date: 2013
Publisher: 0019-0578
Journal: ISA Transactions 
Abstract: 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.
URI: http://hdl.handle.net/10553/44012
ISSN: 0019-0578
DOI: 10.1016/j.isatra.2012.12.006
Source: ISA Transactions[ISSN 0019-0578],v. 52, p. 278-284
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