Vibration sensor based tool condition monitoring using ν support vector machine and locality preserving projection

• Vibration sensor based ν-SVM is presented to realize tool condition monitoring.
• The NN rule based algorithm is proposed to determine the value of ν.
• LPP is utilized to realize dimension reduction of feature vectors.
• The time consuming of NN based ν-SVM is far less than C-SVM with optimization.

Reliable online monitoring of the tool condition is paramount for automatic machining process. C-support vector machine (C-SVM) has got many successful applications in the field of tool wear monitoring. However, the selection of penalty parameter C is usually realized based on optimization process, which increases the training time of the classifier greatly. In this paper, ν support vector machine (ν-SVM) is presented to realize multi categories tool wear classification. In this model, C is replaced by a new parameter ν which represents an upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. At the same time, the nearest neighbor (NN) based rule is proposed to realize the fast selection of ν based on training samples. In addition, to further improve training speed and classification accuracy, locality preserving projection (LPP) method is utilized to reduce the dimension of feature vectors by extracting the lower dimensional manifold characteristics. To testify the effectiveness of the proposed method, milling experiment of Ti6Al4V alloy was carried out and vibration signals corresponding to four kinds of tool wear status were collected. Time domain and frequency domain features are extracted based on wavelet packet decomposition and dimension reduction is realized by using LPP algorithm. Based on the selected features, both C-SVM and ν-SVM are utilized to realize the classification of multi categories tool wear status. The analysis shows that the combination of NN based ν-SVM with LPP can realize faster training of classifier without sacrificing the classification accuracy.