Earticle

초록

영어

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 for improving the reliability of hydraulic systems.

목차

Abstract
 1. Introduction
 2. RBF Networks
 3. FARX Model
  3.1. Structure of ARX
  3.2. Fault Feature Extraction Approach
 4. Fault Diagnosis
 5. Experiment
 6. Summary
 Acknowledgements
 References

키워드

저자

• Xiang-yu He [ State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou, 310027, China, Key Laboratory of Lightweight and Reliability Technology for Engineering Vehicle of Education Department of Hunan Province, Changsha University of Science and Technology, Changsha, Hunan, 410004 China ]
• Yijiao Yang [ Key Laboratory of Lightweight and Reliability Technology for Engineering Vehicle of Education Department of Hunan Province, Changsha University of Science and Technology, Changsha, Hunan, 410004 China ]
• Shanghong He [ Shanghong He2 | Key Laboratory of Lightweight and Reliability Technology for Engineering Vehicle of Education Department of Hunan Province, Changsha University of Science and Technology, Changsha, Hunan, 410004 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605