基于HHT变换和独立分量技术的模态参数识别

Structural modal parameters indentification method based on hilbert-huang transform and independent component technique

HHT方法在识别环境激励下的模态参数时已经有比较高的精确度,并且在工程实际运用中得到了很好的体现。但是,HHT法长期以来都有着许多不足之处:在经验模态分解过程中会出现模态混叠,尖端效应以及虚假模态问题。所以,如何在模态参数识别时减小甚至避免这类问题是至关重要的。主要研究如何减小模态混叠对参数识别的影响。首先加入白噪声对原始信号进行预处理,得到1组新的信号;接着进行EMD分解过程,得到若干个模式函数(IMF),根据IMFs判据来判定其是否是真正的IMFs;然后运用希尔伯特变换及独立分量技术识别出结构的模态参数;最后应用所提方法识别了一个实测飞机模型的模态参数并和实验数据进行对比。结果表明该方法成功解决了HHT方法中的模态混叠现象,并能更加准确地提取信号的各阶模态参数。

HHT method has a relatively high accuracy on modal parameter identification under ambient excitation and has been well reflected in the practical application of engineering. But HHT method has its own shortcomings for a long time. There are some problems such as mode aliasing, false mode in the course of empirical mode decomposition （EMD）. So how to reduce or even avoid such problems is becoming more and more important. This paper studies how to reduce aliasing effects on modal parameter identification. At first, adding white noise is used to pre-process measured primary response signals, the new signals is obtained. Then a series of intrinsic mode functions （IMFs） are separated from （he processed signals by using EMD. The real IMFs are determined by the correlative coefficient between the separated. Af terwards, the Hilbert transform and the irtdependent component technique are employed to identify structural modal pa rameters from the free vibration responses IMFs and the primary signal. The presented method was used to identify mo dal parameters of a measured Aircraft model finally. The results show that the improved method can extract mode pa rameters and transient features from signals more effectively, and some problems existed in the original HHT can be re solved successfully.