中文摘要：

针对滚动轴承早期故障特征信息难以识别以及从小波包分解后的频带不能有效确定并自适应提取共振带的问题,提出了频带幅值熵的概念。在此基础上,将小波包变换和Teager能量谱结合,提出了基于小波包变换自适应Teager能量谱的早期故障诊断方法。该方法首先利用小波包对采集到的振动信号进行分解,并计算各子带的频带幅值熵。然后将熵值按升序排列后依次作为阈值,提取频带幅值熵大于阈值的子带,依据峭度指标确定最佳熵阈值以及小波包最佳分解层数,从而自适应并且有效地提取出共振带。最后对共振带进行Teager能量谱分析,即可从中准确地识别出轴承的故障特征频率。通过信号仿真与实验数据分析验证了该方法的有效性。

英文摘要：

Considering the early fault feature information of rolling bearings is difficult to identify,and form the frequency bands after wavelet packet decomposition can not be effectively determined and adaptive to extract the resonance band,the concept of amplitude entropy of frequency band is proposed.On this basis,the wavelet packet transform and Teager energy spectrum was combined,a rolling bearing early fault feature extraction method is proposed based on wavelet packet transform adaptive Teager energy spectrum.Firstly,the vibration signal was decomposed by wavelet packet,and the frequency amplitude entropy of each subband was calculated.Then,on the basis of kurtosis index to determine the best entropy and the optimal decomposition level of wavelet packet,thus,the resonance band was extracted adaptively and effectively.Finally,the Teager energy spectrum analysis was performed to identify the frequency of the bearing fault.Through the signal simulation and experimental data analysis it verifies the effectiveness of the proposed method.