摘要
由于传统浅层模型对故障的表征能力有限,同时信号特征的提取又过分依靠专家经验,针对这些问题,提出了一种基于深度一维卷积神经网络(D-1DCNN)的轴向柱塞泵故障诊断方法。首先,采集了柱塞泵正常、松靴、滑靴磨损、中心弹簧失效、配流盘磨损5种状态下的振动信号,并将这些信号制作成样本集,加以标签标记,将样本集划分为训练样本与测试样本;然后,将样本输入到D-1DCNN中,进行了训练样本信号的特征提取工作,通过前向传播和反向传播方式得到了D-1DCNN的具体模型;再使用SoftMax分类器对测试样本进行了分类,并对网络模型中的参数进行了调整,得到了柱塞泵故障诊断的准确率值;最后,通过西储大学的轴承故障信号对此进行了仿真对比。研究结果表明:采用该方法对轴向柱塞泵故障进行诊断,其准确率可达到100%;使用D-1DCNN对柱塞泵进行故障诊断时,不需要人工设计或提取特征过程便可实现诊断过程的智能化;对于不同的故障诊断对象,该方法也具备良好的诊断效果,因而具有一定的普适性。
In view of the limited fault characterization ability of traditional shallow models,and the extraction of signal features relying too much on expert experience,a fault diagnosis method for axial piston pump based on deep one-dimension convolution neural network(D-1DCNN)was proposed.Firstly,the vibration signals of piston pump under five states:normal,loose shoe,shoe wear,central spring failure and valve plate wear were collected.The signals were made into a sample set and got labeled.The sample set was divided into training samples and test samples.Then the samples were input into D-1DCNN for feature extraction of training sample signals.The D-1DCNN specific model was obtained by forward propagation and back propagation,the SoftMax classifier was used to classify the test samples,and the parameters in the network model were adjusted to obtain the accuracy of piston pump fault diagnosis.Finally,the simulation comparison of the bearing fault signals of Western Reserve University was carried out.The research results show that the method can achieve 100%diagnostic accuracy for axial piston pump fault diagnosis,using D-1DCNN for fault diagnosis can realize intelligent diagnosis of piston pump without manual design or extraction of features;the method also has good fault diagnosis effect for different objects,and has a certain universality.
作者
徐昌玲
黄家海
兰媛
武兵
钮晨光
马晓宝
李斌
XU Chang-ling;HUANG Jia-hai;LAN Yuan;WU Bing;NIU Chen-guang;MA Xiao-bao;LI Bin(College of Mechanical and Vehicle Engineering,Taiyuan University of Technology,Taiyuan 030000,China;Key Laboratory of New Sensors and Intelligent Control of Ministry of Education,Taiyuan University of Technology,Taiyuan 030000,China;Technology Department,Taiyuan Satellite Launch Center,Taiyuan 030027,China)
出处
《机电工程》
CAS
北大核心
2021年第11期1494-1500,共7页
Journal of Mechanical & Electrical Engineering
基金
山西省科技重大专项资助项目(20181102027,20181102016)
山西省应用基础研究计划面上项目(201901D111054)
山西省青年科技研究基金资助项目(201801D221225)。
