Improved WNN to Rotating Machinery Fault Diagnosis

Author

Xu, Jinli ; Huang, Yuan ; Duan, Ying

Author_Institution

Sch. of Mech. & Electron. Eng., Wu Han Univ. of Technol., WuHan, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

The improved algorithm of WNN based on BP was proposed in this paper. Theoretical analysis and simulation result show it avoids both the blindness of framework designs for BP neural networks and the problem of nonlinear optimizations, such as local optimization. So it can simplify the training of neural networks. It has better abilities in function learning and generalization. This algorithm was successfully applied to rotating machinery fault diagnosis. Therefore it has wide application prospect.

Keywords

backpropagation; fault diagnosis; neural nets; optimisation; production engineering computing; production equipment; BP neural networks; WNN algorithm; backpropagation neural network; function learning; neural network training; nonlinear optimization problem; rotating machinery fault diagnosis; wavelet neural network; Analytical models; Computational modeling; Computer science; Design optimization; Fault diagnosis; Machinery; Neural networks; Neurons; Time frequency analysis; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5364431

Filename

5364431