Title :
Research on Neural Network Integration Fusion Method and Application on the Fault Diagnosis of Automotive Engine
Author :
Zhang, Xiaodan ; Lu, Meng ; Sun, Peigang ; Xu, Guixian ; Zhao, Hai
Author_Institution :
Beijin lnst. of Technol., Beijing
Abstract :
A new fusion model is proposed, which is the combination of integration BP neural networks models and D-S evidence reasoning model, to solve the problems of low precision rate in automotive engine fault diagnosis by traditional expert system. The method of this paper not only realizes feature level fusion of all subjective observation data and expert experiments on different parts of engineer, but also realizes the predominance compensation of different models. In simulation experiment, by comparison between the two methods, this method proposed in the paper can improve diagnosis precision 7.1%more than expert system and reduce time complication degree.
Keywords :
automotive components; backpropagation; case-based reasoning; fault diagnosis; internal combustion engines; mechanical engineering computing; neural nets; BP neural network models; D-S evidence reasoning model; automotive engine; fault diagnosis; feature level fusion; neural network integration fusion method; Application software; Automotive engineering; Computer science; Diagnostic expert systems; Engines; Fault diagnosis; Mathematical model; Mathematics; Neural networks; Uncertainty;
Conference_Titel :
Industrial Electronics and Applications, 2007. ICIEA 2007. 2nd IEEE Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-0737-8
Electronic_ISBN :
978-1-4244-0737-8
DOI :
10.1109/ICIEA.2007.4318455
