Empirical mode decomposition(EMD) is a new signal decomposition method, which could decompose the non-stationary signal into several single-component intrinsic mode functions (IMFs) and each IMF has some physical mean...Empirical mode decomposition(EMD) is a new signal decomposition method, which could decompose the non-stationary signal into several single-component intrinsic mode functions (IMFs) and each IMF has some physical meanings. This paper studies the single trial extraction of visual evoked potential by combining EMD and wavelet threshold filter. Experimental results showed that the EMD based method can separate the noise out of the event related potentials (ERPs) and effectively extract the weak ERPs in strong background noise, which manifested as the waveform characteristics and root mean square error (RMSE).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
基金Natural Science Fund for Colleges and Universities in Jiangsu Province of China grant number: 10 KJB510003+2 种基金Natural Science Fund in Changzhou grant number: CJ20110023 Open Project of the State Key Laboratory of Robotics and System (HIT), and the State Key Laboratory of Cognitive Neurosciences and Learning
文摘Empirical mode decomposition(EMD) is a new signal decomposition method, which could decompose the non-stationary signal into several single-component intrinsic mode functions (IMFs) and each IMF has some physical meanings. This paper studies the single trial extraction of visual evoked potential by combining EMD and wavelet threshold filter. Experimental results showed that the EMD based method can separate the noise out of the event related potentials (ERPs) and effectively extract the weak ERPs in strong background noise, which manifested as the waveform characteristics and root mean square error (RMSE).
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.
