Application of improved DBD algorithm based bp neural network on fault diagnosis for fuel supply system in a certain diesel engine

Author

Feng Fuzhou ; Si Aiwei ; Xing Wei

Author_Institution

Dept. of Mech. Eng., Acad. of Armored Force Eng., Beijing, China

Volume

2

fYear

2011

fDate

10-12 June 2011

Firstpage

469

Lastpage

473

Abstract

In order to overcome the drawbacks of a neural network based on back propagation (BP) algorithm, such as too slow to converge and easy to be trapped into a local minimum, a new modified algorithm is proposed in this paper, in which the grads information of the network are exchanged dynamically in each iteration step, and the increment factor of learning rate and interaction function in delta-bar-delta (DBD) algorithm are improved based on the idea of cross and mutation in Genetic algorithm (GA). The new algorithm has been applied in the fault diagnosis of a fuel supply system in a certain diesel engine successfully.

Keywords

automotive engineering; backpropagation; diesel engines; fault diagnosis; fuel systems; genetic algorithms; mechanical engineering computing; neural nets; DBD algorithm; backpropagation neural network; delta-bar-delta algorithm; diesel engine; fault diagnosis; fuel supply system; genetic algorithm; Artificial neural networks; Diesel engines; Electron tubes; Fuels; Joints; Needles; Valves; DBD algorithm; GA; fault diagnosis; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-8727-1

Type

conf

DOI

10.1109/CSAE.2011.5952510

Filename

5952510