基于小波包分析的小飞轮轴承故障诊断方法研究

Fault Diagnosis of Flywheel Bearings Based on Wavelet Package Analysis

小波包分析具有很强的适应性,特别是对于非平稳振动信号能显示其优越性。实测小飞轮轴承振动信号中含有大量噪声,对此必须进行小波包滤噪提取出有用的信号成分,然后对降噪后的信号进行小波包分解,计算出各子频带内的能量成分,作出小波包能量谱,对能量突出的频带进行进一步分析。实验证明这种方法用于小飞轮轴承故障诊断是有效的、可靠的。

Wavelet packet analysis has a strong adaptability and superiority, especially for the non-stationary vibration signals. The measured vibration signal of flywheel bearing contains a large number of noises, and has to use the wavelet packet noise filtering to extract the useful signal components, and then decomposes the noises in wavelet packet to calculate the energy components of each sub-band. So this paper works out the wavelet packet energy spectrum, and makes a more detailed analysis of the prominent energy band. Experiments prove the fault diagnosis of flywheel bearings effective and reliable.