摘要
为了有效地进行刀具状态监测,提出了一种基于小波分析和支持向量机相结合的刀具故障诊断方法。首先运用小波包对AE信号进行分解和重构,然后提取各个频带里的信号能量值,将该能量值作为特征参数输入到支持向量机,进行学习训练,完成对刀具磨损状态的有效识别。仿真结果表明该方法是有效的。
In order to improve cutting tool condition monitor,a fault-diagnosis method is proposed for cutting tool based on wavelet analysis and support vector machine.Firstly,the AE signals are decomposed and recomposed using wavelet packets.Afterwards,the signal energy values,which are extracted from each frequency band,are used as input features into the support vector machine for completing recognition of the status of the cutting too1.Experiments indicate that the method is effective.
出处
《组合机床与自动化加工技术》
北大核心
2010年第12期65-67,70,共4页
Modular Machine Tool & Automatic Manufacturing Technique
关键词
AE信号
小波分析
支持向量机
故障诊断
AE signals
wavelet analysis
support vector machine
fault diagnosis
