摘要
履带式水仓清理机发生故障时的振动信号传输频率受履带式结构影响,导致故障诊断耗时较长。为了保证水仓清理机机械的稳定运行,提出基于改进经验模态分解(EMD)的煤矿用履带式水仓清理机机械故障诊断方法。利用基于余弦窗函数与波形特征匹配延拓改进EMD算法,分解履带式水仓清理机振动信号;通过小波能量提取振动信号的能量特征;将提取到的能量特征输入支持向量机(SVM)故障诊断模型中,完成水仓清理机故障的诊断。实验结果表明,所提方法的机械故障诊断效果好,准确率高达到0.995,收敛度值达到0.2,诊断效率高。
The transmission frequency of the vibration signal when the crawler type sump cleaning machine fails is affected by the crawler type structure,which leaded to a long fault diagnosis time.In order to ensure the stable operation of the water sump cleaning machine,a mechanical fault diagnosis method based on improved empirical mode decomposition(EMD)for coal mine crawler water sump cleaning machine is proposed.The EMD algorithm was improved based on cosine window function and waveform feature matching continuation to decompose the vibration signal of crawler sump cleaning machine.The energy feature of vibration signal is extracted by wavelet energy,and the extracted energy features is input into the support vector machine(SVM)fault diagnosis model to complete the fault diagnosis.The experimental results show that the mechanical fault diagnosis effect of the proposed method is favorable,the accuracy reaches 0.995,the convergence reaches 0.2,and the diagnosis efficiency is high.
作者
靳现平
徐元涛
刘洋
马平
赵俊达
JIN Xian-ping;XU Yuan-tao;LIU Yang;MA Ping;ZHAO Jun-da(CHN Energy Shendong Coal Group Co.,Ltd.,Shenmu 719315,China;CCTEG Tangshan Research Institute Co.,Ltd.,Tangshan 063000,China)
出处
《煤炭工程》
北大核心
2022年第S01期181-186,共6页
Coal Engineering
关键词
水仓清理机
机械故障诊断
改进EMD算法
小波能量
支持向量机
water sump cleaning machine
mechanical fault diagnosis
improved empirical mode decomposition algorithm
wavelet energy
support vector machine
作者简介
靳现平(1974—),男,河北邯郸人,工程师,现从事煤矿机电管理工作,E-mail:Jxp8206930@163.com。
