Application of the Teager-Kaiser energy operator in bearing fault diagnosis.
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.
Instituto para el Desarrollo Tecnológico y la Innovación en Comunicaciones (IDeTIC), Departamento de Señales y Comunicaciones, Universidad de Las Palmas de Gran Canaria, Sala Software, Edificio Polivalente II, Campus de Tafira, Las Palmas, Spain. phenriquez@gi.ulpgc.es
Patricia
P
Henríquez Rodríguez
Jesús B
JB
Alonso
Miguel A
MA
Ferrer
Carlos M
CM
Travieso
D000465:Algorithms; D003198:Computer Simulation; D019544:Equipment Failure Analysis / Q000295:instrumentation*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012399:Rotation; D060388:Support Vector Machine; D014159:Transducers