基于卷积神经网络的液压缸内泄漏检测

由密封损坏引起的液压缸内泄漏会导致液压系统工作的不稳定。本文作者提出一种基于卷积神经网络的检测方法,先经过仿真得到在无泄漏、小泄漏、中等泄漏和大泄漏4种工况下的液压缸一个腔的压力信号,通过卷积神经网络的学习与训练,使其在不确定工况下通过输入压力信号自动地检测液压缸的泄漏程度。相比于传统的建模方法,文中方法克服了在非线性液压系统中建模难点,只需要采集压力信号,且简单可行,具有很高的可靠性;将该方法与传统的BP神经网络作对比,证明该神经网络的优越性

Convolution Neural Network Based Internal Leakage Fault Diagnosis for Hydraulic Cylinders

Internal leakage which may be caused by seal damage is concerned for leading to unstable operation of the hydraulic systems.To study this issue,a convolution neural network based method to detect the internal leakage fault is proposed.First through simulation,internal pressure signal of a hydraulic cylinder was obtained under the working conditions of four kinds of leakages as no,small,medium and large,after studying and training,then the neural network was used to detect the different internal leakage degrees automatically under uncertain working conditions by input of the signal.Comparing the traditional method based on modelling,this method proposed overcome the drawback of the difficulty in modelling of the nonlinear hydraulic system,sampling of pressure signals was needed only,and it was feasible and simple,with very promising reliability.At last,the traditional back propagation (BP) neural network is compared,which shows the excellence of the convolution neural network.