Acoustic Emission(AE)waveforms contain information on microscopic structural features that can be related with damage of coal rock masses.In this paper,the Hilbert-Huang transform(HHT)method is used to obtain detailed...Acoustic Emission(AE)waveforms contain information on microscopic structural features that can be related with damage of coal rock masses.In this paper,the Hilbert-Huang transform(HHT)method is used to obtain detailed structural characteristics of coal rock masses associated with damage,at different loading stages,from the analyses of the characteristics of AE waveforms.The results show that the HHT method can be used to decompose the target waveform into multiple intrinsic mode function(IMF)components,with the energy mainly concentrated in the c1−c4 IMF components,where the c1 component has the highest frequency and the largest amount of energy.As the loading continues,the proportion of energy occupied by the low-frequency IMF component shows an increasing trend.In the initial compaction stage,the Hilbert marginal spectrum is mainly concentrated in the low frequency range of 0−40 kHz.The plastic deformation stage is associated to energy accumulation in the frequency range of 0−25 kHz and 200−350 kHz,while the instability damage stage is mainly concentrated in the frequency range of 0−25 kHz.At 20 kHz,the instability damage reaches its maximum value.There is a relatively clear instantaneous energy peak at each stage,albeit being more distinct at the beginning and at the end of the compaction phase.Since the effective duration of the waveform is short,its resulting energy is small,and so there is a relatively high value from the instantaneous energy peak.The waveform lasts a relatively long time after the peak that coincides with failure,which is the period where the waveform reaches its maximum energy level.The Hilbert three-dimensional energy spectrum is generally zero in the region where the real energy is zero.In addition,its energy spectrum is intermittent rather than continuous.It is therefore consistent with the characteristics of the several dynamic ranges mentioned above,and it indicates more clearly the low-frequency energy concentration in the critical stage of instability failure.This study well reflects the response law of geophysical signals in the process of coal rock instability and failure,providing a basis for monitoring coal rock dynamic disasters.展开更多
Due to piping vibration, fluid pulsation and other environmental disturbances, variations of amplitude and frequency to the raw signals of vortex flowmeter are imposed. It is difficult to extract vortex frequencies wh...Due to piping vibration, fluid pulsation and other environmental disturbances, variations of amplitude and frequency to the raw signals of vortex flowmeter are imposed. It is difficult to extract vortex frequencies which indicate volumetric flowrate from noisy data, especially at low flowrates. Hilbert-Huang transform was adopted to estimate vortex frequency. The noisy raw signal was decomposed into different intrinsic modes by empirical mode decomposition, the time-frequency characteristics of each mode were analyzed, and the vortex frequency was obtained by calculating partial mode’s instantaneous frequency. Experimental results show that the proposed method can estimate the vortex frequency with less than 2% relative error; and in the low flowrate range studied, the denoising ability of Hilbert-Huang transform is markedly better than Fourier based algorithms. These findings reveal that this method is accurate for vortex signal processing and at the same time has strong anti-disturbance ability.展开更多
Recognizing the underwater targets by the radiated noise information is one of the most significant subjects in the area of underwater acoustics. Based on the theory of auditory perception, a novel recognition approac...Recognizing the underwater targets by the radiated noise information is one of the most significant subjects in the area of underwater acoustics. Based on the theory of auditory perception, a novel recognition approach which consists of the algorithms of Bark-wavelet analysis, Hilbert-Huang transform and support vector machine is proposed. The performance of the proposed method is validated by comparing with traditional method and evaluated by the recognition experiments for SNRs of 0 dB, 5 dB, 10 dB, 15 dB and 20 dB.The results show that the average recognition rate of the method is above 88% and can be increased by 0.75 % to 6.25% under various SNR conditions compared to the baseline system.展开更多
基金Projects(51904167, 51474134, 51774194) supported by the National Natural Science Foundation of ChinaProject(SKLCRSM19KF008) supported by the Research Fund of the State Key Laboratory of Coal Resources and Safe Mining,CUMT,China+5 种基金Project(cstc2019jcyj-bsh0041) supported by the Natural Science Foundation of Chongqing,ChinaProject(2011DA105287-BH201903) supported by the Postdoctoral ScienceFunded by State Key Laboratory of Coal Mine Disaster Dynamics and Control,ChinaProject(2019SDZY034-2) supported by the Key R&D plan of Shandong Province,ChinaProject(2020M670781) supported by the China Postdoctoral Science FoundationProject supported by the Taishan Scholars ProjectProject supported by the Taishan Scholar Talent Team Support Plan for Advantaged&Unique Discipline Areas,China
文摘Acoustic Emission(AE)waveforms contain information on microscopic structural features that can be related with damage of coal rock masses.In this paper,the Hilbert-Huang transform(HHT)method is used to obtain detailed structural characteristics of coal rock masses associated with damage,at different loading stages,from the analyses of the characteristics of AE waveforms.The results show that the HHT method can be used to decompose the target waveform into multiple intrinsic mode function(IMF)components,with the energy mainly concentrated in the c1−c4 IMF components,where the c1 component has the highest frequency and the largest amount of energy.As the loading continues,the proportion of energy occupied by the low-frequency IMF component shows an increasing trend.In the initial compaction stage,the Hilbert marginal spectrum is mainly concentrated in the low frequency range of 0−40 kHz.The plastic deformation stage is associated to energy accumulation in the frequency range of 0−25 kHz and 200−350 kHz,while the instability damage stage is mainly concentrated in the frequency range of 0−25 kHz.At 20 kHz,the instability damage reaches its maximum value.There is a relatively clear instantaneous energy peak at each stage,albeit being more distinct at the beginning and at the end of the compaction phase.Since the effective duration of the waveform is short,its resulting energy is small,and so there is a relatively high value from the instantaneous energy peak.The waveform lasts a relatively long time after the peak that coincides with failure,which is the period where the waveform reaches its maximum energy level.The Hilbert three-dimensional energy spectrum is generally zero in the region where the real energy is zero.In addition,its energy spectrum is intermittent rather than continuous.It is therefore consistent with the characteristics of the several dynamic ranges mentioned above,and it indicates more clearly the low-frequency energy concentration in the critical stage of instability failure.This study well reflects the response law of geophysical signals in the process of coal rock instability and failure,providing a basis for monitoring coal rock dynamic disasters.
基金Project(20030335058) supported by the Special Research Fund for the Doctoral Programof Higher Education of China
文摘Due to piping vibration, fluid pulsation and other environmental disturbances, variations of amplitude and frequency to the raw signals of vortex flowmeter are imposed. It is difficult to extract vortex frequencies which indicate volumetric flowrate from noisy data, especially at low flowrates. Hilbert-Huang transform was adopted to estimate vortex frequency. The noisy raw signal was decomposed into different intrinsic modes by empirical mode decomposition, the time-frequency characteristics of each mode were analyzed, and the vortex frequency was obtained by calculating partial mode’s instantaneous frequency. Experimental results show that the proposed method can estimate the vortex frequency with less than 2% relative error; and in the low flowrate range studied, the denoising ability of Hilbert-Huang transform is markedly better than Fourier based algorithms. These findings reveal that this method is accurate for vortex signal processing and at the same time has strong anti-disturbance ability.
基金Sponsored by Program for New Century Excellent Talents in University ( NCET-08-0459)
文摘Recognizing the underwater targets by the radiated noise information is one of the most significant subjects in the area of underwater acoustics. Based on the theory of auditory perception, a novel recognition approach which consists of the algorithms of Bark-wavelet analysis, Hilbert-Huang transform and support vector machine is proposed. The performance of the proposed method is validated by comparing with traditional method and evaluated by the recognition experiments for SNRs of 0 dB, 5 dB, 10 dB, 15 dB and 20 dB.The results show that the average recognition rate of the method is above 88% and can be increased by 0.75 % to 6.25% under various SNR conditions compared to the baseline system.
