Mechanical Fault Identification Method Based on Vector Power Spectrum Coupled with Radial Basis Probabilistic Networks

Author

Yang Chunyan ; Yang ChunLi ; Wu Chao

Author_Institution

Zhengzhou Univ. of Light Ind., Zhengzhou, China

Volume

2

fYear

2010

fDate

13-14 March 2010

Firstpage

623

Lastpage

626

Abstract

A new mechanical fault identification method coupling vector power spectrum with radial basis probabilistic neural networks (RBPNN) is proposed in the paper. Vector power spectrum is used as eigenvectors, and radial basis probabilistic neural network (RBPNN) is used as a classifier in the new method. The method is used to identify the typical mechanical fault. The result shows that the new method is very effective to identify the fault diagnosis of rotating machinery, and has higher correct identification rate and faster training speed.

Keywords

fault diagnosis; mechanical engineering computing; probability; radial basis function networks; sensor fusion; turbomachinery; vectors; RBPNN; eigenvector; fault diagnosis; mechanical fault identification; radial basis probabilistic neural network; vector power spectrum; Automation; Couplings; Fault diagnosis; Mechatronics; Fault Diagnosis; Information Fusion; Radial Basis Probabilistic Neural Networks (RBPNN); Vector Power Spectrum;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on

Conference_Location

Changsha City

Print_ISBN

978-1-4244-5001-5

Electronic_ISBN

978-1-4244-5739-7

Type

conf

DOI

10.1109/ICMTMA.2010.528

Filename

5459494