Real-time chatter detection is important in improving the surface quality of workpieces in milling. Since the process from stable cutting to chatter is characterized by the progressive variation of the vibration energy distribution, entropy has been utilized to capture the decreasing randomness of vibration signals when chatter occurs. To make such an index more sensitive to transitions of the cutting state, the entropy can be computed based on signal components obtained through signal decomposition techniques. However, the classic empirical mode decomposition (EMD) is difficult to put into practice due to its weak robustness to noises. The up-to-date variational mode decomposition (VMD) has strict requirements on a priori information about the signal and thus is not applicable either. In this paper, a novel method named the iterative Vold-Kalman filter (I-VKF) is proposed under the framework of the greedy algorithm, where the Vold-Kalman filter (VKF), a classic order tracker for rotating machinery, is improved to recursively extract each signal component. In the meantime, a spectrum concentration index–based technique is developed for the estimation of the instantaneous chatter frequency to adaptively determine the filter parameter. Numerical examples demonstrate the superiority of the I-VKF over the original VKF, EMD, and VMD, especially in the presence of strong noises. Combined with the energy entropy of extracted components and an automatically calculated threshold, the proposed strategy greatly helps in timely chatter detection, which has been verified by dynamic simulation and experiments.

实时颤振检测对于提高铣削工件的表面质量非常重要。由于从稳定切削到颤振的过程以振动能量分布的渐进变化为特征，因此利用熵来捕捉颤振发生时振动信号的随机性下降。为了使这样的指数对切割状态的转变更敏感，可以基于通过信号分解技术获得的信号分量来计算熵。然而，经典的经验模式分解（EMD）由于其对噪声的鲁棒性较弱而难以付诸实践。最新的变分模式分解（VMD）对信号的先验信息有严格的要求，因此也不适用。在本文中，在贪婪算法的框架下，提出了一种名为迭代沃尔德-卡尔曼滤波器（I-VKF）的新方法，其中改进了经典的旋转机械阶次跟踪器沃尔德-卡尔曼滤波器（VKF）以递归提取每个信号成分。同时，开发了一种基于频谱集中指数的技术来估计瞬时颤振频率，以自适应地确定滤波器参数。数值例子证明了 I-VKF 优于原始 VKF、EMD 和 VMD，尤其是在存在强噪声的情况下。结合提取分量的能量熵和自动计算的阈值，所提出的策略极大地有助于及时检测颤振，这已通过动态仿真和实验验证。