DocumentCode :
1632512
Title :
Wavelet-Based Bispectra for Motor Rotor Fault Detection
Author :
Yang, D.-M.
Author_Institution :
Dept. of Mech. & Autom. Eng., Kao-Yuan Univ., Kaohsiung
Volume :
1
fYear :
2008
Firstpage :
603
Lastpage :
607
Abstract :
Wavelet-based bispectral analysis is addressed for condition monitoring of induction machines. This advanced signal processing technique combining wavelet analysis and bispectral techniques allows the detection and characterization of non-Gaussian and non-stationary signals with time resolution and the discrimination linear processes from nonlinear ones. In the present investigation, application of this new technique to detect and identify an induction machine´s rotor faults by measuring vibration data and analyzing the nonlinearity of the machine, due to the fact that damaged or faulty machines often generate highly nonlinear signals. The usefulness and statistical robustness of this approach are confirmed in the experiment. The results and analysis indicate that this novel signal processing technique can be effectively applied to motor rotor fault detection.
Keywords :
condition monitoring; fault location; induction motors; rotors; signal detection; signal resolution; spectral analysis; statistical analysis; wavelet transforms; condition monitoring; induction machine; motor rotor fault detection; nonGaussian signal detection; nonlinear signal; nonstationary signal detection; signal processing; statistical analysis; time resolution; vibration data measurement; wavelet-based bispectral analysis; Condition monitoring; Fault detection; Fault diagnosis; Induction machines; Rotors; Signal analysis; Signal processing; Signal resolution; Vibration measurement; Wavelet analysis; Wavelet-based bispectral analysis; fault detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems Design and Applications, 2008. ISDA '08. Eighth International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-0-7695-3382-7
Type :
conf
DOI :
10.1109/ISDA.2008.23
Filename :
4696275
Link To Document :
بازگشت