利用希尔伯特(Hilbert)变换能够得到电力系统振荡中电流信号包络线的特点,根据系统正常运行、纯振荡以及振荡中再故障情况下包络线的不同变化趋势,采用两相电流包络线之差的突变量作为判别量,提出了一种新型的振荡中再故障判别元件,该...利用希尔伯特(Hilbert)变换能够得到电力系统振荡中电流信号包络线的特点,根据系统正常运行、纯振荡以及振荡中再故障情况下包络线的不同变化趋势,采用两相电流包络线之差的突变量作为判别量,提出了一种新型的振荡中再故障判别元件,该判别元件的计算精度不受电力系统振荡时频率变化的影响。大量 ATP 仿真证明了该元件在纯振荡过程中能够可靠不误动,并且可以快速有效地识别系统振荡中发生的各种故障。而且,文中采用的算法实现方便,计算量小,具有在工程中良好的实际应用价值。展开更多
Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on f...Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.展开更多
文摘利用希尔伯特(Hilbert)变换能够得到电力系统振荡中电流信号包络线的特点,根据系统正常运行、纯振荡以及振荡中再故障情况下包络线的不同变化趋势,采用两相电流包络线之差的突变量作为判别量,提出了一种新型的振荡中再故障判别元件,该判别元件的计算精度不受电力系统振荡时频率变化的影响。大量 ATP 仿真证明了该元件在纯振荡过程中能够可靠不误动,并且可以快速有效地识别系统振荡中发生的各种故障。而且,文中采用的算法实现方便,计算量小,具有在工程中良好的实际应用价值。
基金supported by the Major State Basic Research Development of China (Grant No. 2011CB706803)National Natural Science Foundation of China (Grant No. 50875098)Important National Science & Technology Specific Projects of China (Grant No. 2009ZX04014-024)
文摘Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.
