Title :
A Fault Diagnostic Method for EFI Engine Based on MATLAB Software Package
Author :
Du Danfeng ; Guo Xiurong ; Guan Qiang
Author_Institution :
Northeast Forestry Univ., Harbin
Abstract :
At present, the diagnostic instruments used widely here and abroad is not entire, which can not diagnose the mechanical fault without fault code. In order to solve this problem, this paper presents a method for fault diagnosis of electronic fuel injection (EFI) engine using radial basis function (RBF) neural network. By connecting MATLAB software package and ACCESS database, a fault diagnosis program is set up and a fault without code can be found. Meanwhile, the comparison has been done between RBF network and back propagation (BP) network. The simulation experimental results show that the RBF model is more feasible and successful than BP model and makes fault diagnosis easier.
Keywords :
backpropagation; engines; fault diagnosis; fuel systems; mechanical engineering computing; radial basis function networks; software packages; ACCESS database; EFI engine; MATLAB software package; RBF network; back propagation network; electronic fuel injection engine; fault diagnostic method; radial basis function neural network; Databases; Engines; Fault diagnosis; Fuels; Instruments; Joining processes; MATLAB; Mathematical model; Neural networks; Software packages; electronic fuel injection (EFI) engine; fault diagnosis; radial basis function (RBF) neural network;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2008 International Conference on
Conference_Location :
Hunan
Print_ISBN :
978-0-7695-3357-5
DOI :
10.1109/ICICTA.2008.97
