用声发射传感器采集刀具的切削状态信号 ,用小波分析和神经网络技术对信号进行特征提取和仿真训练 ,并建立了基于小波神经网络的刀具故障监测系统。系统对已测得的刀具切削状态信号进行仿真试验 ,结果表明 :系统对刀具故障的预报正确率为 93.3% ,可有效地应用到工程实践中 Acoustic emission sensor was used to acquire the cutting status signal of the cutter. The signal was extracted and simulated by wavelet analysis and neural network technology. And a fault monitoring system based on wavelet neural network was established. The system simulates the measured tool cutting status signals. The results show that the correct rate of the system prediction of the tool failure is 93.3%, which can be effectively applied to engineering practice