对机械故障振动信号的分析结果表明，基于EMD的能量算子解调法能有效地提取机械故障振动信号的特征。
The analysis results show that the energy operator demodulating approach based on EMD can extract the characteristics of mechanical fault vibration signals efficiently.
振动信号能量在局域波时频分布中的变化，是局域波法诊断齿轮磨损故障的特征。
The energy variation of vibration signals is the features of gearbox-case fault diagnosis.
在对采集到的信号降噪后，利用“小波包-能量”法提取特征量，并将其输入到神经网络中进行故障识别。
After noise reduction in the signal, using "wavelet packet-energy" to extract the characteristic vector and input them to the neural network for fault identification.
提出了基于小波包分解频带能量监测法，对滚动轴承的几种典型故障进行了诊断，并且提取特征向量，为后续的神经网络识别作准备。
Propose wavelet packet decomposition frequency-band monitoring method, diagnose typical failures of rolling bearing, and extract eigenvector to prepare follow-up Neural network identification.
提出了基于小波包分解频带能量监测法，对滚动轴承的几种典型故障进行了诊断，并且提取特征向量，为后续的神经网络识别作准备。
Propose wavelet packet decomposition frequency-band monitoring method, diagnose typical failures of rolling bearing, and extract eigenvector to prepare follow-up Neural network identification.
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