Chatter detection in turning using machine learning and similarity measures of time series via dynamic time warping

Chatter detection from sensor signals has been an active field of research. While some success has been reported using several featurization tools and machine learning algorithms, existing methods have several drawbacks including the need for data pre-processing by an expert. In this paper, we present an alternative approach for chatter detection based on K-Nearest Neighbor (KNN) algorithm for classification and the Dynamic Time Warping (DTW) as a time series similarity measure. The used time series are the acceleration signals acquired from the tool holder in a series of turning experiments. Our results show that this approach achieves detection accuracies that can outperform existing methods, and it does not require data pre-processing. We compare our results to the traditional methods based on Wavelet Packet Transform (WPT) and the Ensemble Empirical Mode Decomposition (EEMD), as well as to the more recent Topological Data Analysis (TDA) based approach. We show that in two out of four cutting configurations our DTW-based approach is in the error range of the highest accuracy or attain the highest classification rate reaching in one case as high as 98% accuracy. Moreover, we combine the Approximate and Eliminate Search Algorithm (AESA) and parallel computing with the DTW-based approach to achieve chatter classification in less than 2 s thus making our approach applicable for online chatter detection.