摘要
为实现刀具的实时状态监测,以超高斯函数为基础,构造出一类用递推公式进行小波变换的小波基,提出该系列小波基的优化方法,对其时频特性进行了分析。对刀具AE信号进行递归小波分解,提取特征并应用ART2网络识别刀具状态。结果表明,基于递归小波与ART2网络的刀具状态监测具有鲁棒性强、实时性好的特点。
Real-time tool condition monitoring plays a very important role in the unmanned machining environment. Based on the Super-Gaussian function, a general method of recursive mother wavelet was introduced, and an optimal method of wavelet construction was proposed. The time-frequency characteristics of recursive wavelet were analyzed. The AE signals of tool conditions were decomposed using a recursive wavelet from which the features are extracted and delivered to an ART2 network for fault recognition. The results show that recursive wavelet and ART2 network based tool condition monitoring is robust and real time.
出处
《机床与液压》
北大核心
2007年第5期220-223,共4页
Machine Tool & Hydraulics
