Abstract
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.
Highlights
Hydraulic servo system plays a crucial role in electromechanical systems, like engineering machinery, metallurgical machinery, ships, and other equipment
This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF (CRSR) method
To demonstrate the effectiveness of the proposed CRSR method, validation data of three fault modes is generated through a hydraulic servo system joint simulation model
Summary
Hydraulic servo system plays a crucial role in electromechanical systems, like engineering machinery, metallurgical machinery, ships, and other equipment. For the purpose of enhancing the expressing ability of core information on multiclass feature sets, spatial transformation or importance measurement methods are used [11] Such methods are able to reduce redundancy existing in features and improve learning efficiency while retaining the performance advantages. For the nonlinear signals of complex electromechanical systems, the dimension reduction methods can reduce the scale of input features for fault diagnosis, they change the basic attributes of the feature set. Such situation makes it difficult to give a clear understanding of the obtained feature subset.
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