The increasing availability of sensor data at machine tools makes automatic chatter detection algorithms a trending topic in metal cutting. Two prominent and advanced methods for feature extraction via signal decomposition are wavelet packet transform (WPT) and ensemble empirical mode decomposition (EEMD). We apply these two methods to time series acquired from an acceleration sensor at the tool holder of a lathe. Different turning experiments with varying dynamic behavior of the machine tool structure were performed. We compare the performance of these two methods with support vector machine (SVM), logistic regression, random forest classification and gradient boosting combined with recursive feature elimination (RFE). We also show that the common WPT-based approach of choosing wavelet packets with the highest energy ratios as representative features for chatter does not always result in packets that enclose the chatter frequency, thus reducing the classification accuracy. Further, we test the transfer learning capability of each of these methods by training the classifier on one of the cutting configurations and then testing it on the other cases. It is found that when training and testing on data from the same cutting configuration both methods yield high accuracies reaching in one of the cases as high as $94\%$ and $95\%$, respectively, for WPT and EEMD. However, our experimental results show that EEMD can outperform WPT in transfer learning applications with accuracy of up to $95\%$.

机床上传感器数据可用性的不断提高使自动颤振检测算法成为金属切削中的一个热门话题。小波包变换（WPT）和整体经验模态分解（EEMD）是通过信号分解进行特征提取的两种突出而先进的方法。我们将这两种方法应用于从车床刀架上的加速度传感器获取的时间序列。进行了具有变化的机床结构动态行为的不同车削实验。我们将这两种方法的性能与支持向量机（SVM），逻辑回归，随机森林分类以及结合递归特征消除（RFE）的梯度增强进行了比较。我们还表明，选择具有最高能量比的小波包作为颤动的代表性特征的基于WPT的常见方法并不总是导致包住颤振频率，从而降低了分类精度。此外，我们通过在一个切割配置上训练分类器，然后在其他情况下对其进行测试，来测试每种方法的传递学习能力。结果发现，当对来自相同切割配置的数据进行训练和测试时，两种方法在达到以下情况之一的情况下，都达到了很高的精度。我们通过在一种切割配置上训练分类器，然后在其他情况下对其进行测试，来测试每种方法的传递学习能力。结果发现，当对来自相同切削配置的数据进行训练和测试时，两种方法都可产生高达 我们通过在一种切割配置上训练分类器，然后在其他情况下对其进行测试，来测试每种方法的传递学习能力。结果发现，当对来自相同切削配置的数据进行训练和测试时，两种方法都可产生高达$94％$ 和 $95％$分别用于WPT和EEMD。但是，我们的实验结果表明，EEMD在传递学习应用程序中的性能优于WPT，精度最高可达$95％$。