The importance of rotating frequency in fault diagnosis of roller bearings is analyzed based on formula of fault characteristic frequency.
在故障诊断中的滚子轴承的旋转频率的重要性进行了分析，根据故障特征频率的计算公式。
The character data of the probably fault and machine is acquired by analyzing mechanical fault characteristic frequency and sets configuration.
通过机械故障特征频率分析和机组组态，对可能存在的故障类别和机组类别的特征数据进行提取；
When the model is connected with the analytic database of signal, the fault parameters corresponding with the characteristic frequency can be gained.
模型与信号分析数据库连接后，可得到该特征频率处的故障参数信息。
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。
Wavelet analysis, also called wavelet transform, is a new analysis method with good time frequency localization characteristic, so it is suitable for fault analysis of power system.
小波分析(或称小波变换)是一种新的时频分析方法，它具有良好的时频局部化特性，适用于电力系统故障分析。
The fault characteristic periodic signals of the rolling bearing are in low frequency band and often buried in the noise.
反映滚动轴承故障的特征周期信号处于较低频带内，容易被噪声淹没，难以检测。
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。
After signal local wave decomposition, the information entropy according to time-frequency distribution was established, and it was used as characteristic parameter for fault recognition.
对信号进行局域波分解后，建立基于时频分布的信息熵，以此作为故障识别的参数。
The method can directly detect the fault characteristic signals in low frequency band and achieve fault diagnosis.
该方法直接检测故障的低频特征信号，完成故障诊断。
The method can directly detect the fault characteristic signals in low frequency band and achieve fault diagnosis.
该方法直接检测故障的低频特征信号，完成故障诊断。
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