在声发射信号的处理中使用了时频分析工具—小波包，将信号在不同尺度上分解，以便确定信号在奇异点处的时频特征信息。
In processing the AE signal, the analysis tool of time and frequency-wavelet package is adopted, in order to confirm the characteristics of time and frequency in strange points of the signal.
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 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.
小波包分析的实质是对小波分解的结果作进一步细分，因而具有比小波分解高得多的频域分辨能力。
The essence of wavelet packet analysis is to make further decomposition of wavelet decomposed result, so the analysis will yield much better frequency localization.
不同状态下相同频段信号的小波系数也不相同，因此可用不同频段信号的小波包系数来表征不同信号的特征。依据以上分析给出了一种基于小波包分解的故障特征信息提取方法。
Wavelet packet decomposition technology has been used to analyze the fault signals in ball mill, a kind of fault feature extracting method based on Wavelet packets decomposition is put forward.
分析了用小波包能量分析方法提取故障信号特征向量的方法，并改进算法解决了小波包分解中的混频现象，根据最佳分解树进行了特征选择。
Based on the frequency domain feature, energy eigenvector of frequency domain is presented using wavelet packet analysis method, and the way of best tree is used to choose symptom.
分析了用小波包能量分析方法提取故障信号特征向量的方法，并改进算法解决了小波包分解中的混频现象，根据最佳分解树进行了特征选择。
Based on the frequency domain feature, energy eigenvector of frequency domain is presented using wavelet packet analysis method, and the way of best tree is used to choose symptom.
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