湖南农业大学学报（自然科学版）2024，Vol.50Issue(2)：100-104,126,6.DOI:10.13331/j.cnki.jhau.2024.02.015
基于深度卷积神经网络的单向阀泄漏模式识别
One-way valve leakage pattern recognition based on deep convolution neural network
摘要
Abstract
In SV10PB1-30B hydraulic control check valve,the sensor was used to collect the vibration signals of 10 valve cores in 3 different leakage modes.A deep convolution model and pattern recognition test were performed with different measuring points(upper surface and seat of check valve)and different signal feature extraction methods(original signal,eigenvalue,feature map).The results showed that the fault type could be effectively identified,and the recognition accuracy rate on the verification set was as high as 88.293%with eigenvalues of the axial impact signal and the deep convolutional neural network.The accuracy was 7.79 times based on the feature map and 1.16 times based on the original time domain impact signal.The optimal number of training steps was 100.The model showed optimal classification effects on normal spool and different damaged spool.关键词
单向阀/深度卷积神经网络/故障诊断/模式识别Key words
check valve/deep convolution neural network/fault diagnosis/pattern recognition分类
机械制造引用本文复制引用
郭建政,童成彪..基于深度卷积神经网络的单向阀泄漏模式识别[J].湖南农业大学学报（自然科学版）,2024,50(2):100-104,126,6.基金项目
湖南省重点研发计划项目(2022NK2028) （2022NK2028）
湖南省自然科学基金项目(2020JJ4045) （2020JJ4045）
