基于小波包和EAR模型的铣削颤振特征提取和评价

Wavelet Packet and EAR Model Based Feature Extraction and Assessment of Chatter in Milling

在本文中，设计用振动信号来监控加工过程，提出了一种基于小波包变换的颤振特征提取方法，并实现了颤振特征的自动提取．基于颤振的极限环特性，建立了信号的ＥＡＲ模型．通过ＥＡＲ模型，提出了确定重构信号所需特征小波包数的方法，并评价了小波包颤振特振提取的可行性。通过实测信号分析，验证了以上方法。

Based on wavelet packets transform (WPT), this paper introduces a new method of feature extraction of chatter in milling process by using vibration signal. The realization of the procedure of automatic feature selection for a given process is studied. Considering the fact that chatter is a nonlinear oscillation of the limit cycle type, the exponential autoregressive (EAR) model is provided. Based on this, a method of the determination of the number of feature wavelet packets is proposed, and then the effectiveness of the WPT feature extraction is assessed. The analysis of practical signal is given to show that the proposed method is very effective.