Abstract
With the aim of automatic recognition of weak faults in hydraulic systems, this paper proposes an identification method based on multi-scale permutation entropy feature extraction of fault-sensitive intrinsic mode function (IMF) and deep belief network (DBN). In this method, the leakage fault signal is first decomposed by empirical mode decomposition (EMD), and fault-sensitive IMF components are screened by adopting the correlation analysis method. The multi-scale entropy feature of each screened IMF is then extracted and features closely related to the weak fault information are then obtained. Finally, DBN is used for identification of fault diagnosis. Experimental results prove that this identification method has an ideal recognition effect. It can accurately judge whether there is a leakage fault, determine the degree of severity of the fault, and can diagnose and analyze hydraulic weak faults in general.
Highlights
The method combines multi-scale permutation entropy of fault-sensitive intrinsic mode function (IMF) and deep belief network (DBN), and its specific steps are as follows: Step 1: Process the sample signal x(t) by the empirical mode decomposition (EMD) method, which is decomposed into several IMF components and a residue
This study proposes an identification method that combines multi-scale permutation entropy
This study proposes an identification method that combines multi-scale permutation entropy feature extraction of fault-sensitive IMF with DBN for the automatic recognition of weak faults in feature extraction of fault-sensitive IMF with DBN for the automatic recognition of weak faults in hydraulic systems
Summary
Deep belief network (DBN) is a newly proposed deep learning model [20] It has a strong autonomous learning and reasoning ability that emphasizes learning the hidden representation and highlights the feature expression of data [21]. Leakage faults with three different severities are taken as the research object, and a novel method based on multi-scale permutation entropy of fault-sensitive intrinsic mode function (IMF) and DBN is proposed for the identification and analysis of weak hydraulic faults. Experiments show that this method can effectively detect whether there is fault in a hydraulic system and determine the degree of the fault
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