Neural network as a method to monitor the status of the system, has a strong ability of self-adaptation, fault tolerance, robustness and a high degree of parallel computing capabilities. These features make the method widely used in signal processing (including pattern recognition and intelligent control) and have achieved good control effects. Radial basis function (RBF) neural network was used for state recognition in the study. Radial basis functions use Gaussian functions, and the center and variance of Gaussian functions are obtained by fuzzy-C fuzzy clustering. 1 Feature generation After studying the collected signals, it was found that the variance, maximum spectral peak (200-600 kHz), one-step autoregression coefficient and rms value are the most sensitive to tool wear. Variance s2=(S(Xi-X)(Xi-X))/(N-1) Maximum spectral peak 200-600 kHz One-step auto regression coefficient P=R1/R0, R1=SXIXI-1/N, R0=Autocorrelation The root mean square of the coefficient XRMS = (SXI2/N) 1â„2 2 Radial basis function neural network design Radial basis function neural network structure The radial basis function network has only one hidden layer, the output unit is the linear summation unit, ie the output Is the weighted sum of the output of each hidden unit. The function of the hidden unit uses the radial basis function (RBF). The weight input to the hidden unit is fixed to 1, and only the weight wj=(j=1,2,...,n) between the hidden unit and the output unit is adjustable. . The most commonly used radial basis function is a Gaussian function. There are two adjustable parameters: the center position and the variance (function width parameter). When using these functions, there are three groups of adjustable parameters for the entire network: Base function's center position, variance, and weight of the output unit.

Fig. 1 Radial basis function (RBF) network structure

Figure 2 Neural Network Test Curve

â—‹ Data Points: - A=T:... Best Approximation Curve Figure 3 Linear Regression

**Table RBF network and BP network performance comparison**training method Network type training time (s) Number of training steps Fuzzy-C clustering algorithm RBF network 1.20 7 Improved BP algorithm BP network 130.42 2016 Figure 3 is the radial basis function network output and the actual tool Wear value linear regression. In the figure, A is the actual wear value of the tool: T is the output of the RBF network. From the right table, the RBF network training speed is much higher than the BP network of the improved algorithm. 4 Conclusions Using the rms value, variance, one-step autocorrelation coefficient and maximum peak value (200-600kHz) of the acquired signal as feature quantities, the tool wear value can be estimated effectively. In real-time, on the microcomputer with a main frequency of 166MHz, the state recognition (35ms) from the data processing to the radial basis function neural network takes about 200ms in total. In practical applications, we collect data every time. The time interval can be set to 1s, 2s, etc. Therefore, this study can meet the real-time requirements.