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Volume 2018, Article ID 8740989, 14 pages
Research Article

Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF

1School of Reliability and Systems Engineering, Beihang University, Beijing, China
2Science & Technology on Reliability and Environmental Engineering Laboratory, Beijing, China
3Research and Development Center, China Academy of Launch Vehicle Technology, Beijing, China

Correspondence should be addressed to Wen-jin Zhang; ten.haey@kojwzaaub

Received 28 October 2017; Revised 5 January 2018; Accepted 14 January 2018; Published 18 February 2018

Academic Editor: Gangbing Song

Copyright © 2018 Yu Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Playing an important role in electromechanical systems, hydraulic servo system is crucial to mechanical systems like engineering machinery, metallurgical machinery, ships, and other equipment. Fault diagnosis based on monitoring and sensory signals plays an important role in avoiding catastrophic accidents and enormous economic losses. This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF (CRSR) method. From the point of view of feature selection, the scheme utilizes CRSR method to determine the most stable feature combination that contains the most adequate information simultaneously. Based on the feature selection structure of ReliefF, CRSR employs feature integration rules in the compressed domain. Meanwhile, CRSR substitutes information entropy and fuzzy membership for traditional distance measurement index. The proposed CRSR method is able to enhance the robustness of the feature information against interference while selecting the feature combination with balanced information expressing ability. To demonstrate the effectiveness of the proposed CRSR method, a hydraulic servo system joint simulation model is constructed by HyPneu and Simulink, and three fault modes are injected to generate the validation data.