This study was performed to investigate the spectral characteristics of micro-seismic signals observed during the rupture of coal. Coal rupture micro-seismic observations were obtained on a test system that included a...This study was performed to investigate the spectral characteristics of micro-seismic signals observed during the rupture of coal. Coal rupture micro-seismic observations were obtained on a test system that included an electro-hydraulic servo pressure tester controlled by a YAW microcomputer, a micro-seismic sensor, a loading system, and a signal collection system. The results show that the micro-seismic signal increases with increasing compressive stress at the beginning of coal rupture. The signal remains stable for a period at this stage. A large number of micro-seismic signals appear immediately before the main rupture event. The frequency of micro-seismic events reaches a maximum immediately after the coal ruptures. Micro-seismic signals were decomposed into several Intrinsic Mode Functions (IMF's) by the empirical mode decomposition (EMD) method using a Hilbert-Huang transform (HHT). The main fre- quency band of the micro-seismic signals was found to range from 10 to 100 Hz in the Hilbert energy spectrum and from marginal spectrum calculations. The advantage of applying an HHT is that this can extract the main features of the signal. This fact was confirmed by an HHT analysis of the coal micro-seis- mic signals that shows the technique is useful in the field of coal rupture.展开更多
In this paper, a novel method that integrates the improved empirical mode decomposition （EMD） and signal energy algorithm is proposed to estimate the dominant oscillation parameters and corresponding mode shape. Fir...In this paper, a novel method that integrates the improved empirical mode decomposition （EMD） and signal energy algorithm is proposed to estimate the dominant oscillation parameters and corresponding mode shape. Firstly, the EMD with symmetrical extrema extension （SEE） is utilized to decompose the measured data from wide area measurement system （WAMS） into a finite set of intrinsic mode functions （1MFs）. Then, the signal energy algorithm is used to calculate the approximate oscillation parameters of the IMFs. The nodes involved the dominant oscillation mode are classified based on the calculated frequency and reasonable threshold. Furthermore, for the dominant oscillation mode, the IMF with maximum mean amplitude is defined as the reference. Next, the relative phases （RPs） between the reference IMF and other 1MFs are calculated in order to identify the negative and positive oscillation groups. According to the values of RPs, the coherent group and corresponding node contribution factor （NCF） can be identified, and the dominant approximate mode shape （AMS） can also be determined. The efficiency of the proposed approach is tested by applying it to synthetic signal and measured data from the simulation model.展开更多
基金support for this work provided by the National Science and Technology Planning Project (No. 2009BAK54B03)the National Natural Science Foundation of China (No. 50834005)
文摘This study was performed to investigate the spectral characteristics of micro-seismic signals observed during the rupture of coal. Coal rupture micro-seismic observations were obtained on a test system that included an electro-hydraulic servo pressure tester controlled by a YAW microcomputer, a micro-seismic sensor, a loading system, and a signal collection system. The results show that the micro-seismic signal increases with increasing compressive stress at the beginning of coal rupture. The signal remains stable for a period at this stage. A large number of micro-seismic signals appear immediately before the main rupture event. The frequency of micro-seismic events reaches a maximum immediately after the coal ruptures. Micro-seismic signals were decomposed into several Intrinsic Mode Functions (IMF's) by the empirical mode decomposition (EMD) method using a Hilbert-Huang transform (HHT). The main fre- quency band of the micro-seismic signals was found to range from 10 to 100 Hz in the Hilbert energy spectrum and from marginal spectrum calculations. The advantage of applying an HHT is that this can extract the main features of the signal. This fact was confirmed by an HHT analysis of the coal micro-seis- mic signals that shows the technique is useful in the field of coal rupture.
文摘In this paper, a novel method that integrates the improved empirical mode decomposition （EMD） and signal energy algorithm is proposed to estimate the dominant oscillation parameters and corresponding mode shape. Firstly, the EMD with symmetrical extrema extension （SEE） is utilized to decompose the measured data from wide area measurement system （WAMS） into a finite set of intrinsic mode functions （1MFs）. Then, the signal energy algorithm is used to calculate the approximate oscillation parameters of the IMFs. The nodes involved the dominant oscillation mode are classified based on the calculated frequency and reasonable threshold. Furthermore, for the dominant oscillation mode, the IMF with maximum mean amplitude is defined as the reference. Next, the relative phases （RPs） between the reference IMF and other 1MFs are calculated in order to identify the negative and positive oscillation groups. According to the values of RPs, the coherent group and corresponding node contribution factor （NCF） can be identified, and the dominant approximate mode shape （AMS） can also be determined. The efficiency of the proposed approach is tested by applying it to synthetic signal and measured data from the simulation model.
