The characteristic frequency band of the fault can be identified by wavelet packet decomposition and its energy spectrum conveniently, and the quantification analysis are then performed.
应用小波包分解及其能量谱直观地识别出故障的特征频带，并进行了量化分析。
The characteristic frequency band of the fault could be identified by wavelet packet decomposition and its energy spectrum conveniently, at the same time, quantification analysis were performed.
应用小波包分解及其能量谱直观地识别出故障的特征频带，并进行了量化分析。
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.
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。
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