Research Article Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF Yu Ding , 1,2 Fei Wang , 1,2 Zhen-ya Wang , 3 and Wen-jin Zhang 1,2 1 School of Reliability and Systems Engineering, Beihang University, Beijing, China 2 Science & Technology on Reliability and Environmental Engineering Laboratory, Beijing, China 3 Research and Development Center, China Academy of Launch Vehicle Technology, Beijing, China Correspondence should be addressed to Wen-jin Zhang; buaazwjok@yeah.net 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. Tis 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. Tis 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. Te 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 efectiveness 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. 1. Introduction Hydraulic servo system plays a crucial role in electrome- chanical systems, like engineering machinery, metallurgical machinery, ships, and other equipment. Failures of hydraulic servo system caused by severe and complex conditions may lead to catastrophic accidents and enormous economic losses. Fault diagnosis based on monitoring and sensory signals is able to classify the current state of complex systems, which plays a key role in performance evaluation [1]. Feature set extracted from signals is an important index to refect the fault mechanism and performance evolution laws. Te quality of feature set plays a key role in improving the generalization ability of fault identifcation [2]. Te common feature extraction methods are time-frequency index extrac- tion, wavelet analysis, Hilbert transform, Dufng oscillator, and so on. Despite their respective applicable conditions and limitations, those methods are able to mine the health characteristics of the system from multiaspect [3, 4]. Same as machine learning, features extracted from images, speeches, and other signals ofen have certain correlations and hidden mutual infuences. Information expressed by a single feature is usually inadequate, which can be greatly improved when the single feature is aggregated with others [5]. Similarly, due to the nonlinearity, instability, and nonconformity of complex electromechanical systems, the expression of the information on individual feature is ofen one-sided. Tus, a new challenge is how to utilize those features more efectively and efciently, in other words, how to obtain the feature set that expresses the information sufciently by eliminating the redundant and negatively correlated features [6–9]. To tackle the challenge mentioned above, on the premise of existing feature extraction techniques, feature process- ing techniques including feature selection and dimension reduction have gradually become an important research focus. Both feature selection and dimension reduction can reduce the scale of feature set by obtaining a set of principal variables. Such techniques ofen use a variety of feature Hindawi Complexity Volume 2018, Article ID 8740989, 14 pages https://doi.org/10.1155/2018/8740989