In view of the non-stationary characteristics of acoustic emission signal of tool wear, and the slow convergence rate of learning algorithm and easily dropping into the local minimum value for back propagation neural networks, a novel method of tool wear state identification based on empirical mode decomposition and least squares support vector machine was proposed. Firstly, the empirical mode decomposition method was used to decompose the collected acoustic emission signals into a number of stationary intrinsic mode function, and then autoregressive model of each intrinsic mode function was established respectively. Finally, auto regression model coefficients were selected to constitute the feature vector. The feature was divided into two groups, one group was used to train the least squares support vector machine and the other was used to identify the tool wear state. The identification result proves that this method is superior to neural network, and it has a higher identification rate. It is proved that this method is efficient and feasible.
Guan Shan, Wang Longshan, Nie Peng.Identification method of tool wear based on empirical mode decomposition and least squares support vector machine[J] JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2011,V37(2): 144-148
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