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

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。

Based on Multi-sensor Fault Diagnosis of Rolling Bearings

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 current 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.