摘要
运用神经网络技术,设计开发了一种基于虚拟仪器平台的轴承故障检测系统。在LabVIEW软件平台上,分别对轴承正常和待检测状态信号进行时域分析并提取其特征值,创建神经网络的训练样本和被测样本,并代入MATLAB节点中的神经网络进行故障诊断。试验结果表明,该系统能正确、快速地判断出被测的轴承是否正常,识别率达100%,当被测轴承诊断出故障时,系统还可发出报警提示。
A fault detection system for bearings based on virtual instrument platform is designed and developed by using neural network technology.The system is developed on LabVIEW software platform,in which both the normal state sig-nal and the signal to be detected are analyzed in time-domain to extract the feature values,and create the trained and tested samples of neural network,and then the trained and tested samples are put into neural network of MATLAB node to realize fault diagnosis.The experimental results show that the system is able to judge whether the bearing state is nor-mal or not accurately and quickly,with a 100% recognition rate,and the system alarms and prompts when a fault oc-curs.
出处
《轴承》
北大核心
2014年第5期53-56,共4页
Bearing
基金
浙江省公益性技术应用研究计划项目(2013C31098)
关键词
滚动轴承
故障检测
特征值提取
神经网络
虚拟仪器
rolling bearing
fault detection
feature value extraction
neural network
virtual instrument
