Support vector machine based online intelligent chatter detection

Abstract

In order to detect chatter in the process of turning, an online intelligent chatter detection method is proposed. In this method, least squares one class support vector machine (LS-OC-SVM) is used to extract a hyper plane as an optimal description of training objects. Chatter is detected by computing the distance between the sample to be tested and the hyper plane. Sparse online LS-OC-SVM is proposed based on coherence criterion and partitioned matrix inversion, so that features information can be stored in the feature library which is also called dictionary. The detection model can be evolved continuously through the online update of feature library. In the application of chatter detection, firstly, feature vector is constructed for chatter detection based on node energy ratios of the third level of wavelet packet decomposition. Then, initial detection model and feature library are trained by using offline feature vectors as input. In the online detection scheme, the detection model is evolved while feature library is updated. The experimental results show that the online evolution model performs better than offline model in the cutting chatter detection. Chatter detection accuracy of the online evolution model is 99.04%, which is better than offline model whose detection accuracy is 96.74%.