滚动轴承的故障特征提取技术与方法研究  

Rolling Bearing Fault Feature Extraction Technique and Method

作　　者：刘春光[1] 谭继文[1] 张驰[1] 

机构地区：[1]青岛理工大学,山东青岛266033

出　　处：《煤矿机械》2010年第4期236-237,共2页Coal Mine Machinery

摘　　要：基于小波分析获取轴承故障频率,并以该频率为中心点向前2.5 Hz和向后2.5 Hz的频带变化作为滚动轴承的特征值。通过归一化处理,将轴承内圈、外圈和滚动体的故障频带的能量作为故障特征参量,建立起故障频带能量与滚动轴承状态的映射关系,进而应用神经网络进行故障诊断。Based on wavelet analysis for bearing fauh frequencies, and to the frequency for the center forward and backward 2.5 Hz bands 2.5 Hz change as bearing the characteristics of the value. By normalizing, the bearing inner ring, outer ring and rolling bodies of the failure frequency band energy as the fault feature parameters and establish a rolling bearing fault frequency energy and the state mapping, and thus application of neural network fault diagnosis, and effectively raise the bearing troubleshooting.

关 键 词：故障诊断 LABVIEW MATLAB 能量统计 滚动轴承 

分 类 号：TH133.33[机械工程—机械制造及自动化] TP306[自动化与计算机技术—计算机系统结构]