小波包分解与Fuzzy ART神经网络在磨削振动监测中的应用

Application of Wavelet Packet and Fuzzy ART Neural Network to Vibration Exception Monitoring for Grinding Process

针对磨削加工的特点,通过小波包进行振动信号细化分解,提取各尺度能量作为特征量.利用无导师学习的Fuzzy ART神经网络进行振动异常的辨识,在发生未知模式振动异常时,网络将产生新的类报警.与传统监测方法相比,该方法能对已知和未知的振动异常进行辨识报警,在实际磨削过程监控应用中效果良好.

In this paper wavelet packet technique is used to decompose the vibration signals of grinding process. Since these decomposed signals in every band have different energy, they can be used to represent the vibration exception with fuzzy ART neural network. The fuzzy ART neural network can generate a new clustering to give an alarm when vibration exception of unknown pattern appears. Compared with traditional methods this method ＇can identify the known and unknown pattern exceptions of grinding process. The result of practical application shows that this method is very efficient.