摘要
轴向柱塞泵是液压动力系统的重要组成部分,由于其发生故障时会产生严重的危害,所以对其进行故障诊断是非常有必要的。然而大量的工程实践表明,轴向柱塞泵往往会同时在不同的部位,以不同的形式表现为复合故障。由于轴向柱塞泵复合故障振动信号的多分量耦合调制特征及特征参数较难确定,所以针对此问题,提出了一种基于格拉姆角差场与深度残差网络相结合(GADF-ResNet)的轴向柱塞泵复合故障诊断方法。首先,对轴向柱塞泵原始振动信号进行了格拉姆角差场(GADF)转换,将其转换为二维数组,将数组以灰度图形式存储,得到了特征样本,并将其分为训练集与测试集,以多标签的方式进行了标记;然后,将样本输入到深度残差网络(ResNet)中,通过前向传播和反向传播方式确定了网络最佳结构和参数;最后,采用实验的方式,通过测试集验证了该模型的可行性和鲁棒性。实验结果表明:采用基于GADF-Resnet的轴向柱塞泵复合故障诊断方法对轴向柱塞泵的复合故障进行识别,其准确率可以达到87%以上。研究结果表明,该方法可以有效地识别轴向柱塞泵的复合故障。
Axial piston pump was an important part of the hydraulic power system.Due to the serious hazards caused by the failure of the axial piston pump,it was necessary to carry out fault diagnosis of the axial piston pump.However,a large number of engineering practices was showed that the axial piston pump often presents composite faults in different forms at different parts at the same time.Because the multi-component coupling modulation characteristics and characteristic parameters of the composite fault data of the axial piston pump were difficult to determine,a compound fault diagnosis method for the axial piston pump based on Gramian angular difference field-deep residual network(GADF-ResNet)was proposed.Firstly,the original vibration signal of the axial piston pump was converted into a two-dimensional array by Gramian angular difference field(GADF),and the array was stored in the form of a grayscale image to obtain feature samples,which were divided into training sets and test sets,and marked in the form of multiple labels.Then,it was inputted the samples into deep residual network(ResNet)and determined the best network structure and parameters through forward propagation and back propagation.Finally,feasibility and robustness of the model was verified through test sets and experiments.The experimental results show that the compound fault identification accuracy of the axial piston pump based on GADF-Resnet can reach more than 87%.The results show that this method can effectively identify the compound faults of axial piston pump.
作者
袁科研
兰媛
黄家海
马晓宝
王君
李国彦
李利娜
YUAN Ke-yan;LAN Yuan;HUANG Jia-hai;MA Xiao-bao;WANG Jun;LI Guo-yan;LI Li-na(School of Mechanical and Transportation Engineering,Taiyuan University of Technology,Taiyuan 030024,China;Key Laboratory of New Sensors and Intelligent Control of Ministry of Education,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《机电工程》
CAS
北大核心
2023年第6期945-951,共7页
Journal of Mechanical & Electrical Engineering
基金
国家自然科学青年科学基金资助项目(51905369)
山西省关键核心技术和共性技术研发攻关专项(2020XXX001)
山西省科技重大专项(20181102016)
山西省应用基础研究计划青年科技研究基金资助项目(202103021223090)。
关键词
液压传动系统
容积泵
复合故障
格拉姆角差场
深度残差网络
多标签
hydraulic transmission system
positive displacement pump
compound fault
Gramian angular difference field(GADF)
deep residual network(ResNet)
multi-label
