双树复小波包和谱峭度在齿轮故障诊断中的应用

针对故障齿轮振动信号的非平稳和调制特性,提出了基于双树复小波包变换和谱峭度的齿轮故障诊断方法.首先,利用双树复小波包变换将原始振动信号分解为若干个不同频带的信号分量,选择与原始信号相关系数大的分量进行阈值降噪并重构;然后,对降噪后的信号利用谱峭度所得的峭度图选择最佳的带宽和频带中心进行相应的带通滤波处理;最后,将带通滤波后的信号作平方包络和傅里叶变换,即可得到信号的包络解调谱,从而提取故障特征信息.通过对试验和工程实际的齿轮故障信号分析表明:双树复小波包变换和谱峭度结合的方法可有效地提取齿轮故障特征信息,进而实现故障识别,验证了方法的可行性和有效性.

Application of Gear Fault Diagnosis Based on Dual-tree Complex Wavelet Packet Transform and Spectral Kurtosis

Aiming at non-stationary and modulation characteristics of gear fault vibration signals,a fault diagnosis method was proposed based on dual-tree complex wavelet packet transform and spectral kurtosis. First,the original vibration signal was decomposed into several different frequency band components by dual-tree complex wavelet packet transform,some components that have bigger correlation coefficient were de-noised by the threshold. Second,the best bandwidth and band center of band-pass filter were determined through fast kurtosis diagram of spectral kurtosis. Finally, the envelope demodulation spectrum of filter signal could be obtained by square envelope and Fourier transforms,then the fault information was effectively extracted. The analysis of the gear fault signals shows that the fault feature information of the gear can be effectively extracted to identify the fault,and the proposed method is effective and feasible.