A new method for gyroscope fault diagnosis based on CGA RBFNN and multi-wavelet entropy

Author

Yu Ji ; Peng He ; Deyun Zhou ; Jichuan Huang

Author_Institution

Northwestern Polytech. Univ., Xi´an, China

fYear

2013

fDate

20-22 Dec. 2013

Firstpage

39

Lastpage

43

Abstract

An original method based on CGA (Genetic Algorithm based on Cloud-model) RBFNN (Radial Basis Function Neural Network) is proposed for the online fault diagnosis of gyroscope. Based on the information entropy and wavelet transform theory, wavelet energy entropy (WEE) and wavelet time entropy (WTE) are extracted to be the input of RBFNN. Besides, CGA is used to optimize the parameters of RBFNN. The simulation results show that this new method can reach an efficient search for global convergence, and prevent it from similar problem of partial efficiency in traditional genetic algorithm (TGA). After trained, the RBFNN can accomplish the online fault diagnosis of gyroscope accurately and quickly.

Keywords

computerised navigation; fault diagnosis; genetic algorithms; gyroscopes; inertial navigation; neural nets; radial basis function networks; wavelet transforms; CGA; RBFNN; WEE; WTE; genetic algorithm based on cloud-model; gyroscope fault diagnosis; inertial navigation; information entropy; integrated navigation system; radial basis function neural network; wavelet energy entropy; wavelet time entropy; wavelet transform theory; Biological cells; Entropy; Fault diagnosis; Generators; Gyroscopes; Information entropy; Wavelet transforms; CGA; RBFNN; fault diagnosis; gyroscope; wavelet energy entropy (WEE); wavelet time entropy (WTE);

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conference on

Conference_Location

Shengyang

Print_ISBN

978-1-4799-2564-3

Type

conf

DOI

10.1109/MEC.2013.6885047

Filename

6885047