Application of Wavelet Packet Analysis in Turbine Fault Diagnosis

Author

Peng, Yue-hui ; Xu, Xiao-gang ; Zhao, He-xiang

Author_Institution

Dept. of Sci. & Technol., North China Electr. Power Univ., Baoding

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

2897

Lastpage

2900

Abstract

Experimental platform is used to simulate typical faults of turbine. Based on the frequency domain feature, energy eigenvector of frequency domain is presented in the wavelet packet analysis method, and the way of best tree is used to choose symptom. Finally, the fault states are recognized using neural network, and the simulations show that it makes a good performance with the method

Keywords

eigenvalues and eigenfunctions; fault diagnosis; frequency-domain analysis; neural nets; power engineering computing; turbines; wavelet transforms; fault state recognition; frequency domain energy eigenvector; frequency domain feature; neural network; turbine fault diagnosis; wavelet packet analysis method; Algorithm design and analysis; Cybernetics; Discrete wavelet transforms; Fault diagnosis; Frequency domain analysis; Machine learning; Signal analysis; Turbines; Wavelet analysis; Wavelet domain; Wavelet packets; Wavelet transforms; Fault diagnosis; best tree; neural networks; symptom extraction; wavelet packet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.259077

Filename

4028556