Title :
The Fault Diagnosis for Electro-Hydraulic Servo Valve Based on the Improved Genetic Neural Network Algorithm
Author :
Fu, Lian-dong ; Chen, Kui-sheng ; Yu, Jun-sheng ; Zeng, Liang-Cai
Author_Institution :
Coll. of Machinery & Autom., Wuhan Univ. of Sci. & Technol.
Abstract :
The paper analyzes the merits and drawbacks of the genetic algorithm and BP neural network, combines with the improved genetic algorithm and BP neural network to obtain a new algorithm. The new algorithm is used in the fault diagnosis of electro-hydraulic servo valve and justified its validity, accuracy and rapidity by experiment. The BP algorithm, the conventional GA-BP algorithm and the improved GA-BP algorithm are compared by the data of experiment. It is shown the superiority of the improved GA-BP algorithm in the fault diagnosis field
Keywords :
backpropagation; electrohydraulic control equipment; fault diagnosis; genetic algorithms; servomechanisms; valves; BP neural network; electro-hydraulic servo valve; fault diagnosis; genetic neural network algorithm; Control systems; Convergence; Cost function; Cybernetics; Educational institutions; Fault diagnosis; Genetic algorithms; Machine learning; Neural networks; Servomechanisms; Telecommunication control; Valves; Electro-hydraulic servo valve; Fault diagnosis; Genetic algorithm; Neural network;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.259153
