基于奇异值分解的铣削力信号处理与铣床状态信息分离

利用连续截断信号构造矩阵,通过奇异值分解可以将信号表示为一系列分量信号的简单线性叠加,证明了各分量之间是两两正交的,且具有零相位偏移特性.根据分量信号的信息量可以确定合理的矩阵结构.对铣削力信号的处理实例表明,奇异值分解方法分离出机床主轴旋转基频近乎完整的时域波形,分辨出两个频率很接近的信号分量,发现信号中隐藏的调幅现象,证实机床的爬行并确定爬行频率.最后与小波变换的结果进行比较,表明这一方法对铣削力信号的分离效果优于小波变换.

PROCESSING OF MILLING FORCE SIGNAL AND ISOLATION OF STATE INFORMATION OF MILLING MACHINE BASED ON SINGULAR VALUE DECOMPOSITION

A signal can be decomposed into the linear sum of series of component signals by singular value decomposition (SVD) when matrix is created by continuously intercepting signal. It's proved that these component signals are orthogonal each other and have characteristic of zero-phase shift. Matrix structure can be rationally determined according to information amount of component signals. Then this SVD method is applied to the processing of a milling force signal and the results show that waveform of fundamental frequency of principal axis of milling machine is isolated completely and two component signals, whose frequencies are very close, are also isolated. Furthermore, the phenomenon of amplitude modulation hidden in this signal is discovered, the crawl of milling machine is confirmed and crawl frequency is got. The results compared with wavelet transform show that for this milling force signal, the SVD method has the much better effect of signal isolation than wavelet transform.