基于DT-CWT自适应Teager能量谱的轴承早期故障诊断

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,将双树复小波变换和Teager能量谱结合,提出了基于双树复小波自适应Teager能量谱的早期故障诊断方法。首先,利用双树复小波将采集到的振动信号分解为不同频带的子信号,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阈值,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。

Early Fault Diagnosis of Rolling Bearing Based on Dual-tree Complex Wavelet Transform Adaptive Teager Energy Spectrum

Aiming at the early fault feature information of rolling bearings is difficult to identify, and the parameter setting of band-pass filter depends on the user experience, which makes the resonance frequency band not be effectively determined and extracted, the concept of amplitude entropy of frequency band is proposed. On this basis, the dual-tree complex wavelet transform and Teager energy spectrum are combined, and a rolling bearing early fault feature extraction method is proposed based on dual-tree complex wavelet transform adaptive Teager energy spectrum. Firstly, original fault signals are decomposed into several different frequency components through dual-tree complex wavelet decomposition, and the frequency amplitude entropy of each sub-band is calculated. Then the entropy are arranged in ascending order and in turn as a threshold to extract the entropy value greater than the threshold value of the sub bands. The optimal threshold is determined based on the kurtosis index, thus the resonance band is extracted adaptively and effectively. Finally, the fault characteristic frequency of the bearing could be accurately identified from the energy spectrum of the resonance band. The signal simulation and experimental data analyses verify the effectiveness of the proposed method.