基于HHT和SVM的矿山轴承故障在线监测技术  

On Line Monitoring Technology of Mine Bearing Fault Based on HHT and SVM

作　　者：叶小川[1] YE Xiao-chuan(Sichuan Engineering Technical College, Deyang 618000, China)

机构地区：[1]四川工程职业技术学院,四川德阳618000

出　　处：《煤炭技术》2018年第7期329-332,共4页Coal Technology

摘　　要：矿山轴承故障是矿业安全生产的隐患之一,轴承故障信号在线监测能够及时诊断轴承故障,排除安全隐患。采集轴承运行时的振动信号,通过希尔伯特黄变换方法分析振动信号。将振动信号首先进行EMD分解得到轴承故障信号的多层IMF分量,然后进行Hilbert变换得到信号的瞬时频率和边际谱频带能量。将各层IMF分量的平均频率MIF与各层IMF分量的能量比作为特征量,送入SVM中进行训练,可以实现矿山轴承的故障在线监测。The fault of mine bearing is one of the hidden troubles in the safety production of the mining industry. The on-line monitoring of bearing fault signal can diagnose the bearing fault in time and eliminate the hidden danger of safety. The vibration signals are analyzed by Hilbert Huang transform, when the vibration signals are collected during the running of the bearing. First, the vibration signal is decomposed by EMD, and the multi-layer IMF component of the bearing fault signal is obtained. Then the Hilbert transform is used to get the instantaneous frequency and the marginal spectrum band energy of the signal. The energy ratio of the average frequency MIF of each IMF component and the IMF component of each layer is taken as the characteristic achieve on-line monitoring of mine bearing fault. quantity and sent to SVM for training. It can

关 键 词：矿山轴承 故障监测 希尔伯特黄变换 支持向量机 

分 类 号：TH133.3[机械工程—机械制造及自动化] TH165.3