基于图的拉普拉斯矩阵的塑料编织机轴承故障诊断研究  

Research on Bearing Fault Diagnosis of Plastic Braiding Machine Based on Graph Laplacian Matrix

作　　者：赵泉[1] ZHAO Quan(Liaoning University of International Business and Economics,Dalian 116052,China)

机构地区：[1]辽宁对外经贸学院,辽宁大连116052

出　　处：《塑料科技》2020年第9期100-103,共4页Plastics Science and Technology

摘　　要：针对时域故障信号难以诊断的问题,提出基于图的拉普拉斯矩阵变换的注塑编织机轴承故障诊断方法。通过拉普拉斯矩阵变换将时域信号转化为图域,将时域的快速傅立叶变换思想引入图信号处理中,将转化所得图信号进行图像的傅立叶变换(GFT)得到故障振动信号的阶次图,根据所得阶次计算故障振动信号的故障频率,由此进行轴承的故障诊断。经仿真及实例分析结果表明:相比将时域信号直接进行快速傅里叶变换的故障诊断方法,本实验方法故障识别能力更强。Aiming at the problem that time-domain fault signals are difficult to diagnose, a fault diagnosis method of injection knitting machine bearings based on graph Laplacian matrix transformation is proposed. Transform the time domain signal into the graph domain through Laplace matrix transformation, introduce the idea of fast Fourier transform in the time domain into the graph signal processing, and perform the graph Fourier transform(GFT) on the converted graph signal to obtain the fault vibration signal the order diagram. According to the obtained order, the fault frequency of the fault vibration signal is calculated, and thus the fault diagnosis of the bearing is carried out. The simulation and example analysis results show that: compared with the fault diagnosis method that directly performs the fast Fourier transform of the time domain signal, this experimental method has stronger fault identification ability.

关 键 词：图的拉普拉斯矩阵 图像的傅立叶变换 图信号 故障诊断 

分 类 号：TQ619.6[化学工程—精细化工]