基于小波包自适应Teager能量谱的滚动轴承早期故障诊断

发布时间：2018-05-05 19:17

本文选题：滚动轴承 + 频带幅值熵　；参考：《机械强度》2017年04期

【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。
[Abstract]:Aiming at the difficulty of identifying the early fault characteristic information of rolling bearing and the problem that the frequency band after small wave packet decomposition can not be effectively determined and adaptively extracted the concept of frequency band amplitude entropy is put forward. On this basis, combining wavelet packet transform with Teager energy spectrum, an early fault diagnosis method based on wavelet packet transform adaptive Teager energy spectrum is proposed. Firstly, the wavelet packet is used to decompose the collected vibration signal, and the amplitude entropy of each sub-band is calculated. Then the entropy is arranged in ascending order as the threshold, and the sub-band whose amplitude entropy is larger than the threshold is extracted. The best entropy threshold and the best decomposition layer number of wavelet packet are determined according to the kurtosis index, and the resonance band is extracted adaptively and effectively. Finally, the bearing fault characteristic frequency can be accurately identified by Teager energy spectrum analysis. The validity of the method is verified by signal simulation and experimental data analysis.
【作者单位】：内蒙古科技大学机械工程学院;山东交通职业学院泰山校区机电工程系;
【基金】：国家自然科学基金项目(21366017) 内蒙古高等学校科学研究项目(NJZY16154) 内蒙古科技大学创新基金项目(2015QDL10)资助~~
【分类号】：TH133.33

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