Affiliation:
1. School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang, Hebei 050043, China
2. Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
Abstract
Most of the existing hydraulic cylinder internal leakage detection methods are laboratory testing methods, mainly the pressure-holding method, measurement of hydraulic cylinder settlement method, and measuring cup measurement method. The internal leakage of the hydraulic cylinder affects the damping characteristics of the control system. Therefore, real-time internal leakage detection plays an important role in the control characteristics. This paper first proposes a wavelet analysis-based extraction of hydraulic cylinder internal leakage fault eigenvalues for analysis, that is, data processing. A convolutional neural network-based detection method is also proposed, in which the pressure signal of a chamber of a hydraulic cylinder is first obtained through simulation under four operating conditions: no leakage, small leakage, medium leakage, and large leakage. Compared to traditional modelling methods, the method overcomes the difficulties in modelling nonlinear hydraulic systems, requires only pressure signal acquisition, is simple and reliable, and is compared with traditional BP neural networks to demonstrate its superiority.
Funder
Wuhan University of Science and Technology
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
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