基于EMD平均能量法的滚动轴承故障诊断

文中提出了一种用于滚动轴承故障诊断的系统一基于EMD和BP神经网络相结合的诊断系统。该方法首先对原始振动信号进行小波包预处理，提高信噪比，从而得到更适合研究的故障振动信号。然后再对信号进行EMD，分解得到IMF分量，对几个感兴趣的IMF进行分析，获得每个IMF分量的平均能量，作为BP神经网络的输入向量，由此训练神经网络，实现了对滚动轴承故障的智能诊断，并用实际的滚动轴承故障数据进行了验证。图6表2参12

Fault Diagnosis of Rolling Bearing Based on EMD Average Energy Method

The paper presents a fault diagnosis system based on the combination of EMD and BP Neural network. Firstly, in order to improve the signal to noise ratio, the method uses wavelet packet to pretreatment with the original vibration signal, so it will get the more suitable fault vibration signal for research. Secondly, decomposes the signal with EMD, then analysis the several interesting IMF taking the average energy of each IMF part obtained as input vector of BP neural network to train the BP network and realizes intelligent diagnosis of rolling bearing fault testified with real rolling bearing fauh data.