基于振动信号EMD和ICA的刀具破损识别

针对多齿铣削过程中振动信号的特点,提出了一种基于经验模态分解(EMD)和独立分量分析(ICA)相结合的方法,对混叠在振动信号中的铣刀破损信号进行分离。对振动信号进行经验模态分解提取出信号中的所有本征模函数,然后应用fastICA对所提取出的本征模函数进行独立分量分析。利用该方法对铣削加速度振动数据进行了分析,试验表明,该方法可以提取出混合信号中与刀具破损状态相关的故障特征频率成分。

Vibration Signal Processing for Tool Breakage Monitoring Based on EMD and ICA

An approach based on empirical mode decomposition(EMD) and independent component analysis(ICA) was presented to deal with the blind source separation(BSS) problem of vibration signals in the process of face milling.This method was used to separate tool breakage signal in vibration signals.EMD method was used to extract all intrinsic mode functions(IMF) in the vibration signals which had been acquired from face milling processes,then deal with those IMFs using FastICA,and obtain a lot of independent components.The method was applied to analyze the acceleration vibration signals in the process of face milling.Analysis result shows that this method can extract the characteristic frequency components related to tool breakage from mixed signals.