Abstract
Internal leakage is the most common failure of hydraulic cylinder; when it increases, it decreases volumetric efficiency, pressure and speed of the hydraulic cylinder, and can seriously affect the normal operation of the hydraulic cylinder, so it is important to measure it, especially to measure it online. Firstly, the principle of internal leakage online measurement is proposed, including the online measurement system, the fixed mode of the strain gauge and the mathematical model of the flow-strain signal conversion. Secondly, an experimental system is established to collect internal leakages and strain values, and the data is processed. Finally, the convolutional neural network (CNN), BP neural network (BPNN), Radial Basis Function Network (RBF), and Support Vector Regression (SVR) are used to predict the hydraulic cylinder leakage; the comparison of experimental results show that the CNN has high accuracy and high efficiency. This study provides a new idea for online measurement of small flow on other hydraulic components.
Highlights
Hydraulic cylinder is the actuator in the system, and its failure directly affects the normal operation and life of the system
Learning from the above research on the fault diagnosis of hydraulic cylinder leakage, this paper studies how to realize the online measurement of hydraulic cylinder leakage
Possible reason: the strain gauge has high sensitivity, and the strain caused by micro flow at low pressure is extremely small, so above strain value is mainly caused by external noise, such as hydraulic cylinder vibration caused by pressure loading
Summary
Hydraulic cylinder is the actuator in the system, and its failure directly affects the normal operation and life of the system. The fault diagnosis of hydraulic cylinder leakage is divided into two types: Model-based methods and data-driven methods [1]. The traditional data-driven methods are divided into three steps: feature extraction, feature selection, and building classifiers. The convolutional neural network (CNN) is one of the most effective deep learning methods, applied on fault detection and diagnosis of hydraulic [20]. The rest of the paper is structured as follows: Section 2 contains the online measurement system of leakage and the mathematical model of flow-strain signal, Section 3 builds an experimental acquisition system to obtain strain data and the values of internal leakage.
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