基于神经网络的液压缸内泄漏在线测量研究

内泄漏是液压缸最常见故障,严重影响液压缸的正常工作,因此对其在线测量显得尤为重要。提出内泄漏在线测量的工作原理,包括在线测量系统、应变片的固定方式和流量-应变信号转换的数学模型,并搭建实验系统采集内泄漏和应变数据并进行数据处理。分别采用BP神经网络和卷积神经网络对液压缸内泄漏进行预测,结果表明,卷积神经网络准确度高、效率高,为其他液压元件微小流量的在线测量提供一种新的思路。

Online Measurement of Hydraulic Cylinder Leakage Based on Neural Network

 Internal leakage is the most common failure of hydraulic cylinder, which can seriously affect the normal work of hydraulic cylinder, so it is important 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 leakage and strain value, and the data is processed. Finally, the BP neural network and convolutional neural network (CNN) 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.