Title :
The Intelligent System Design of Remote Fault Diagnosis of Reducer Based on GA and NN
Author_Institution :
Dept. of Mech. Eng., Nantong Univ., Nantong, China
Abstract :
Reducer failure was analyzed and by use of BP neural network in the paper. Model of failure diagnosis was established. By using genetic algorithms, the value of neural networks, the threshold, and the network structure were optimized. Genetic neural network model was applied to the system design of remote reducer fault diagnosis. To compare training error curve of BP neural network with genetic neural network, it was shown that genetic neural network in the training of speed and accuracy higher than the neural network training model.
Keywords :
backpropagation; failure analysis; fault diagnosis; genetic algorithms; mechanical engineering computing; neural nets; product design; BP neural network; GA; NN; genetic neural network model; intelligent system design; network structure; neural network training model; reducer failure; remote reducer fault diagnosis; training error curve; Competitive intelligence; Computer errors; Condition monitoring; Failure analysis; Fault diagnosis; Genetic algorithms; Hardware; Intelligent systems; Neural networks; Remote monitoring; The Intelligent System design of Remote Fault Diagnosis of Reducer Based on GA and NN;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.240
