基于双树复小波包自适应Teager能量谱的滚动轴承早期故障诊断

发布时间：2018-10-12 08:30

【摘要】：针对滚动轴承早期故障特征信息难以识别以及带通滤波器参数设置依赖使用者经验等造成共振带不能有效确定并自适应提取的问题,提出了频带幅值熵的概念。在此基础上,将双树复小波包变换和Teager能量谱结合,提出了基于双树复小波包变换自适应Teager能量谱的早期故障诊断方法。该方法首先利用双树复小波包将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或等于阈值的子带,依据峭度指标确定最佳熵阈值和双树复小波包最佳分解层数,从而自适应并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。通过信号仿真与工程实验数据分析验证了该方法的有效性与优越性。
[Abstract]:Aiming at the difficulty of identifying the fault feature information in the early stage of rolling bearing and the fact that the parameter setting of bandpass filter depends on the user's experience the resonance band can not be effectively determined and adaptively extracted the concept of frequency band amplitude entropy is put forward. Based on the combination of bitree complex wavelet packet transform and Teager energy spectrum, an early fault diagnosis method based on adaptive Teager energy spectrum of bitree complex wavelet packet transform is proposed. In this method, the collected vibration signals are decomposed by using a double tree complex wavelet packet, and the frequency band amplitude entropy of each sub-band is calculated. Then the entropy value is arranged in ascending order as the threshold value, and the sub-band whose amplitude entropy is greater than or equal to the threshold value is extracted, and the best entropy threshold and the best decomposition layer number of the double tree complex wavelet packet are determined according to the kurtosis index. Thus the resonance band can be extracted adaptively and efficiently. Finally, the bearing fault characteristic frequency can be accurately identified by Teager energy spectrum analysis. The validity and superiority of this method are verified by signal simulation and engineering experiment data analysis.
【作者单位】：内蒙古科技大学机械工程学院;
【基金】：国家自然科学基金项目（21366017）内蒙古自然科学基金重大项目（20130302）
【分类号】：TH165;;TH133.3;;TP206

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