Title :
Fault diagnosis of hydraulic system based on improved BP neural network technology
Author :
Zhang Yinshuo ; Xia Jun ; Li Lei
Author_Institution :
No.3 Dept., Nanjing Artillery Acad., Langfang, China
Abstract :
A fault diagnosis model with BP network for a certain hydraulic system were described. The realization process of the fault diagnosis based on the improved BP algorithm was discussed. According to the experiment, the improved BP network has better learning ability, higher convergence rate ability and higher stability of learning and memory. The diagnosis results indicate that the presented diagnosis method has higher reliability and can attain the expected results, which can be applied to fault diagnosis of hydraulic system.
Keywords :
backpropagation; fault diagnosis; hydraulic systems; mechanical engineering computing; neural nets; BP neural network technology; convergence rate ability; fault diagnosis; hydraulic system; learning ability; learning stability; memory stability; Artificial intelligence; Biological neural networks; Fault diagnosis; Hydraulic systems; Mathematical model; Neurons; BP algorithm; fault diagnosis; hydraulic system; neural network;
Conference_Titel :
Measurement, Information and Control (ICMIC), 2013 International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4799-1390-9
DOI :
10.1109/MIC.2013.6757933