Research on CVT fault diagnosis system based on artificial neural network

Author

Zhou, Meilan ; Zhang, Shige ; Wen, Jiabin ; Wang, Xudong

Author_Institution

Coll. of Electr.&Electron. Eng., Harbin Univ. of Sci. & Technol., Harbin

fYear

2008

fDate

3-5 Sept. 2008

Firstpage

1

Lastpage

5

Abstract

To accomplish the demand of continuously variable transmission (CVT) fault diagnosis, the structure of CVT fault diagnosis system is built and the application model of Back-Propagation Neural Network is established aiming at the features of CVT faults. The structure and 3 algorithms of network are devised. The network proposed is simulated and the results are analyzed in detail. The simulation results indicate that the fault diagnosis system based on Back-Propagation neural network with momentum and self-adaptive learning rate algorithm is effective.

Keywords

backpropagation; fault diagnosis; mechanical engineering computing; neural nets; power transmission (mechanical); variable speed gear; CVT fault diagnosis system; artificial neural network; backpropagation neural network; continuously variable transmission; self-adaptive learning rate algorithm; Artificial neural networks; Circuit faults; Computer interfaces; Fault diagnosis; Feedforward neural networks; Multi-layer neural network; Neural networks; Propulsion; Signal processing algorithms; Vehicles; Back-Propagation Neural Network; continuously variable transmission (CVT); fault diagnosis; momentum and self-adaptive learning rate algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Vehicle Power and Propulsion Conference, 2008. VPPC '08. IEEE

Conference_Location

Harbin

Print_ISBN

978-1-4244-1848-0

Electronic_ISBN

978-1-4244-1849-7

Type

conf

DOI

10.1109/VPPC.2008.4677422

Filename

4677422