An intelligent chatter detection method based on EEMD and feature selection with multi-channel vibration signals

Abstract

Chatter detection in metal machining is important to ensure good surface quality and avoid damage to the machine tool and workpiece. This paper presents an intelligent chatter detection method in a multi-channel monitoring system comprising vibration signals in three orthogonal directions. The method comprises three main steps: signal processing, feature extraction and selection, and classification. The ensemble empirical mode decomposition (EEMD) is used to decompose the raw signals into a set of intrinsic mode functions (IMFs) that represent different frequency bands. Features extracted from IMFs are ranked using the Fisher discriminant ratio (FDR) to identify the informative IMFs, and those features with higher FDRs are selected and presented to a support vector machine for classification. Single-channel strategies and multi-channel strategies are compared in low immersion milling of titanium alloy Ti6Al4V. The results demonstrate that the

On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition

“…In several papers, these features are ranked and are utilized as the input for the machine learning classifiers. Support Vector Machine (SVM) algorithm is the most common classifier used for chatter classification [10,23,24,25,26,27,28]. Other less common classifiers include quadratic discrimination analysis [29], Hidden Markov Model (HMM) [30], generalized HMM [31], and logistic regression [32] (cf.…”

“…Ji et al [23] proposed EMD to both eliminate noise from milling vibration signals and to extract features from informative Intrinsic Mode Functions (IMF). Chen et al [24] used top-ranked features extracted from the IMFs obtained from EEMD for machining state detection. Li et al [34] used the energy spectrum of the IMFs as features for chatter detection.…”

“…Li et al [34] used the energy spectrum of the IMFs as features for chatter detection. The resulting features are ranked by using Fisher Discriminant Ratio (FDR) [24] and, when the number of features is high, recursive feature elimination (RFE) is used to reduce the number of features [25]. Although EMD/EEMD is typically applied to vibration signals, Liu et al [26] also used EMD to extract features from the servo motor current time series.…”

Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector

“…The standard approach for chatter recognition from cutting signals has mostly focused on extracting features by decomposing the time series and combining them with supervised learning algorithms-most commonly Support Vector Machine (SVM). The two most widely used decompositions are Wavelet Packet Transform (WPT) [6][7][8][9][10][11][12][13][14] and Ensemble Empirical Mode Decomposition (EEMD) [14][15][16][17][18]. Both WPT and EEMD require manually preprocessing the signal to identify the most informative parts of the signal which carry chatter signatures, which is characterized by the part of the decomposition whose spectrum contain the chatter frequency.…”

“…Once the informative decompositions are obtained, they are used to compute several time and frequency domain features for chatter classification. Many times the resulting features are too many and they overfit the model; therefore, the traditional tools are often equipped with a feature ranking process to prune the features' vector [7,14,16].…”