摘要
根据煤矿机械振动信号高低频组成成分变化规律的差异,提出了一种基于经验模态分解(EMD)和支持向量机(SVM)的煤矿机械振动信号组合预测方法。将滚动轴承振动信号进行EMD分解,得到相对平稳的本征模态函数(IMF)分量,并将波动程度相近的IMF分量进行重构,得到高频子序列和低频子序列,采用SVM分别对高频子序列和低频子序列进行预测,将2个预测结果叠加,得到最终预测值。选取轴承实验数据对组合预测方法的有效性进行验证,结果表明该方法的均方根误差、平均绝对误差和平均绝对百分比误差均小于直接预测方法。将该组合预测方法应用于某选煤厂主井带式输送机滚动轴承状况预测,预测结果与实际情况相符。
According to variation differences of high frequency and low frequency components of coal mine machinery vibration signal,a combined vibration signal prediction method of coal mine machinery based on empirical mode decomposition(EMD)and support vector machine(SVM)is proposed.The vibration signal of rolling bearing is decomposed by EMD to obtain relatively stable instrinsic mode function(IMF)components,and the IMF components with similar degree of the fluctuation are reconstructed to obtain high-frequency and low-frequency subsequences.The high-frequency subsequence and low-frequency subsequence are predicted by SVM respectively,and then the final prediction value is obtained after superposing the two prediction results.The bearing experimental data are selected to verify effectiveness of the method.The results show that the root mean square error,average absolute error and average absolute percentage error of the method are smaller than that of the direct prediction method.The results show that the root mean square error,average absolute error and average absolute percentage error of the combined predition method are all smaller than those of direct prediction method.The combined prediction method is applied to condition prediction of rolling bearing of the belt conveyor in main shaft of a coal preparation plant,and the prediction results are consistent with actual situation.
作者
肖雅静
李旭
郭欣
XIAO Yajing;LI Xu;GUO Xin(CCTEG Tiandi Science&Technology Co.,Ltd.,Beijing 100013,China)
出处
《工矿自动化》
北大核心
2020年第3期100-104,共5页
Journal Of Mine Automation
基金
中国煤炭科工集团有限公司科技创新创业资金专项项目(2018MS023)。
作者简介
肖雅静(1983-),女,河北唐山人,工程师,博士,研究方向为机械故障诊断及矿山数字化,E-mail:xiaoyajing001@163.com。
