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A hybrid fault diagnosis approach for hydraulic systems by using radial basis function networks

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成果类型:
期刊论文
作者:
He, Xiang-yu;Yang, Yijiao;He, Shanghong
作者机构:
[He, Xiang-yu] State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou, China
[Yang, Yijiao; He, Shanghong; He, Xiang-yu] Key Lab. of Lightweight and Reliability Technology for Engineering Vehicle, Education Department of Hunan Province, Changsha University of Science and Technology, Changsha, Hunan, China
语种:
英文
关键词:
Auto-regressive with extra outputs (ARX) model;Fault diagnosis;Fuzzy logic;Hydraulic system;Radial basis function neural networks
期刊:
International Journal of Control and Automation
ISSN:
2005-4297
年:
2014
卷:
7
期:
12
页码:
165-176
机构署名:
本校为其他机构
院系归属:
汽车与机械工程学院
摘要:
To improve the reliability of hydraulic systems, a fault diagnosis approach based on radial basis function (RBF) networks was proposed in this paper. According to the target fault features extracted from a fuzzy auto-regressive with extra outputs (FARX) model, RBF networks serve as a fault classifier and the output of the RBF networks is the result of fault diagnosis. Several typical faults of hydraulic systems were used to test the fault diagnosis approach. Experiment results showed that the fault diagnosis approach is feasible and effective...

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