Abstract: To study the effects of the wavelet transform in intensive operational modal parameter identification, a mode system with three degrees of freedom containing two intensive frequencies was established. Applying narrow-band white noise excitation and using the improved Morlet wavelet as the continuous wavelet transform base function, the modal parameter identification of this system was simulated. It was found that reducing bandwidth can improve the frequency resolution effect and decouple the dense modals when applying the continuous wavelet transform method to modal parameter identification. However, it also exacerbated the problem of edge effect which can reduce the accuracy for the dense modal recognition. In order to suppress the edge effect, the length of the useful part of the transformation signal was extended by using the SVM technique. Simulation results show that the better recognition accuracy can be obtained with this method. Finally, through identifying the first two modal parameters of a grinder mill, the feasibility and effectiveness of the method were verified.

Key words: vibration and wave, parameter identification, modal decoupling, edge effects, wavelet ridge

摘要：

为了研究小波变换对密集工作模态参数识别效果，构建一个含有密频成分的三自由度系统，应用窄带白噪声模拟环境激励，采用改进的Morlet小波作为连续小波变换的基函数。研究发现，降低小波函数带宽可以提高频率分辨率，解耦密集模态，但同时也加剧边缘效应问题，影响参数识别精度。为此，文章采用支持向量机（SVM）小样本预测技术对信号进行延拓，先增加信号的可用长度，变换之后再截取有用部分，使得边缘效应问题得到抑制。仿真结果表明，此方法可以得到较高的识别精度。最后，通过对磨机前两阶宻频模态进行识别，验证该方法的可行性与有效性。

关键词: 振动与波, 参数识别, 模态解耦, 边缘效应, 小波脊线