基于多传感器信息融合技术在滚动轴承故障诊断中的应用  

Based on Multi-sensor Fault Diagnosis of Rolling Bearings

作　　者：刘春光[1] 谭继文[1] 战卫侠[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6期252-254,共3页Coal Mine Machinery

摘　　要：为了提高滚动轴承故障诊断的准确性,运用了将多传感器信息融合技术用于故障诊断的检测方法。由电流传感器和加速度传感器采集电流信号和振动信号,经小波变换等预处理后通过能量算子获得故障特征值,再经过动量改进BP神经网络进行训练,实现对滚动轴承故障的准确诊断。通过实验证明该方法可行。In order to improve the accuracy of fault diagnosis of rolling bearings, put forward a multisensor data fusion technology for fault diagnosis of the detection method. By the current sensor and acceleration sensors capture curent signal and vibration signal, wavelet transform pre-treated by the operator obtained by energy failure characteristic value, and then through the momentum of improved BP neural network training, in order to achieve an accurate diagnosis of rolling bearing failure. The experiments show that the method is feasible.

关 键 词：小波分析 电流信号 加速度信号 能量统计 故障诊断 

分 类 号：TH133.33[机械工程—机械制造及自动化] TP39[自动化与计算机技术—计算机应用技术]