Hilbert-Huang Transform (HHT) is a newly developed powerful method for nonlinear and non-stationary time series analysis. The empirical mode decomposition is the key part of HHT, while its algorithm was protected by N...Hilbert-Huang Transform (HHT) is a newly developed powerful method for nonlinear and non-stationary time series analysis. The empirical mode decomposition is the key part of HHT, while its algorithm was protected by NASA as a US patent, which limits the wide application among the scientific community. Two approaches, mirror periodic and extrema extending methods, have been developed for handling the end effects of empirical mode decomposition. The implementation of the HHT is realized in detail to widen the application. The detailed comparison of the results from two methods with that from Huang et al. (1998, 1999), and the comparison between two methods are presented. Generally, both methods reproduce faithful results as those of Huang et al. For mirror periodic method (MPM), the data are extended once forever. Ideally, it is a way for handling the end effects of the HHT, especially for the signal that has symmetric waveform. The extrema extending method (EEM) behaves as good as MPM, and it is better than MPM for the signal that has strong asymmetric waveform. However, it has to perform extrema envelope extending in every shifting process.展开更多
With the significant improvement of microgrid technology, microgrid has gained large-scale application.However, the existence of intermittent distributed generations, nonlinear loads and various electrical and electro...With the significant improvement of microgrid technology, microgrid has gained large-scale application.However, the existence of intermittent distributed generations, nonlinear loads and various electrical and electronic devices causes power quality problem in microgrid, especially in islanding mode. An accurate and fast disturbance detection method which is the premise of power quality control is necessary. Aiming at the end effect and the mode mixing of original Hilbert-Huang transform(HHT), an improved HHT with adaptive waveform matching extension is proposed in this paper. The innovative waveform matching extension method considers not only the depth of waveform, but also the rise time and fall time. Both simulations and field experiments have verified the correctness and validity of the improved HHT for power quality disturbance detection in microgrid.展开更多
Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the dam...Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.展开更多
Emprical mode decomposition(EMD) is a method and principle of decomposing signal dealing with Hilbert-Huang transform (HHT) in signal analysis, while directly-mean EMD is an improved EMD method presented by N.E.Huang,...Emprical mode decomposition(EMD) is a method and principle of decomposing signal dealing with Hilbert-Huang transform (HHT) in signal analysis, while directly-mean EMD is an improved EMD method presented by N.E.Huang, the inventor of HHT, which is aimed at solving the problems of EMD principle. Although the directly-mean HMD method is very remarkable with its advantages and N. E. Huang has given a method to realize it, he did not find the theoretic evidence of the method so that the feasibility of the idea and correctness of realizing the directly-mean EMD method is still indeterminate. For this a deep research on the forming process of complex signal is made and the involved stationary point principle and asymptotic stationary point principle are demonstrated, thus some theoretic evidences and the correct realizing way of directly-mean EMD method is firstly presented. Some simulation examples for demonstrating the idea presented are given.展开更多
A new method is proposed to analyze multi-component linear frequency modulated （LFM） signals, which eliminates cross terms in conventional Wigner-Ville distribution （WVD）. The approach is based on Radon transform ...A new method is proposed to analyze multi-component linear frequency modulated （LFM） signals, which eliminates cross terms in conventional Wigner-Ville distribution （WVD）. The approach is based on Radon transform and Hilbert-Huang transform （HHT）, which is a recently developed method adaptive to non-linear and non-stationary signals. The complicated signal is decomposed into several intrinsic mode functions （IMF） by the empirical mode decomposition （EMD）, which makes the consequent instantaneous frequency meaningful. After the instantaneous frequency and Hilbert spectrum are computed, multi-component LFM signals detection and parameter estimation are obtained using Radon transform on the Hilbert spectrum plane. The simulation results show its feasibility and effectiveness.展开更多
A new method to identify flow regime in two-phase flow was presented, based on signal processing of differential pressure using Hilbert Huang transform （HHT）. Signals obtained from a Venturi meter were decomposed in...A new method to identify flow regime in two-phase flow was presented, based on signal processing of differential pressure using Hilbert Huang transform （HHT）. Signals obtained from a Venturi meter were decomposed into different intrinsic mode functions （IMFs） with HHT, then the energy fraction of each intrinsic mode and the mean value of residual function were calculated, from which the rules of flow regime identification were summarized. Experiments were carried out on two-phase flow in the horizontal tubes with 50mm and 40mm inner diameter, while water flowrate was in the range of 1.3m^3.h^-1 to 10.5m^3.h^-1, oil flowrate was from 4.2m^3.h^-1 to 7.0m^3.h^-1 and gas flowrate from 0 to 15m^3.h^-1. The results show that the proposed rules have high precision for single phase, bubbly, and slug, plug flow regirne identification, which are independent of not only properties of two-phase fluid. In addition, the method can meet the need of industrial application because of its simple calculation.展开更多
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 w展开更多
The ESMD method can be seen as a new alternate of the well-known Hilbert-Huang transform (HHT) for non-steady data processing. It is good at finding the optimal adaptive global mean fitting curve, which is superior to...The ESMD method can be seen as a new alternate of the well-known Hilbert-Huang transform (HHT) for non-steady data processing. It is good at finding the optimal adaptive global mean fitting curve, which is superior to the common least-square method and running-mean approach. Take the air-sea momentum flux investigation as an example, only when the non-turbulent wind components is well extracted, can the remainder signal be seen as actual oscillations caused by turbulence. With the aid of —5/3 power law for the turbulence, a mode-filtering approach based on ESMD decomposition is developed here. The test on observational data indicates that this approach is very feasible and it may greatly reduce the error caused by the non-turbulent components.展开更多
In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-H...In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-Huang Transform to extract modal parameters for closely spaced modes and low-energy components. The proposed method is applied to a simulated airplane model built in Automatic Dynamic Analysis of Mechanical Systems software. The results demonstrate that the identified modal parameters are in good agreement with the baseline model.展开更多
ABSTRACT Current computerized pulse diagnosis is mainly based on pressure and photoelectric signal. Considering the richness and complication of pulse diagnosis information, it is valuable to explore the feasibility o...ABSTRACT Current computerized pulse diagnosis is mainly based on pressure and photoelectric signal. Considering the richness and complication of pulse diagnosis information, it is valuable to explore the feasibility of novel types of signal and to develop appropriate feature representation for diagnosis. In this paper, we present a study on computerized pulse diagnosis based on blood flow velocity signal. First, the blood flow velocity signal is collected using Doppler ultrasound device and preprocessed. Then, by locating the fiducial points, we extract the spatial features of blood flow velocity signal, and further present a Hilbert-Huang transform-based method for spectrum feature extraction. Finally, support vector machine is applied for computerized pulse diagnosis. Experiment results show that the proposed method is effective and promising in distinguishing healthy people from patients with cho- lecystitis or nephritis.展开更多
In this paper we discuss the use of the Hilbert-Huang transform（HHT） to enhance the time-frequency analysis of microtremor measurements. HHT is a powerful algorithm that combines the process of empirical mode decomp...In this paper we discuss the use of the Hilbert-Huang transform（HHT） to enhance the time-frequency analysis of microtremor measurements. HHT is a powerful algorithm that combines the process of empirical mode decomposition（EMD） and the Hilbert transform to compose the HilbertHuang spectrum that contains the time-frequency-energy information of the recorded signals. HHT is an adaptive algorithm and does not require the signals to be linear or stationary. HHT is advantageous for analyzing microtremor data, since observed microtremors are commonly contaminated by nonstationary transient noises close to the recording instruments. This is especially true when microtremors are measured in an urban environment. In our data processing HHT was used to（1） eliminate the unwanted short-duration transient constituents from microtremor data and use only the coherent portion of the data to carry out the widely used horizontal to vertical spectral ratio（H/V） method;（2） identify and eliminate the continuous industrial noise in certain frequency band; and（3） enhance the H/V analysis by using the Hilbert-Huang spectrum（HHS）. The efficacy of this proposed approach is demonstrated by the examples of applying it to microtremor data acquired in the metropolitan Beijing area.展开更多
基金This study is supported by the National Natural Science Foundation of China(NSFC)under contract Nos 49790010,40076010 and 49634140,National Key Basic Research and Development Plan in China under contract No.G1999043701)and the OCEAN-863 Project of China.
文摘Hilbert-Huang Transform (HHT) is a newly developed powerful method for nonlinear and non-stationary time series analysis. The empirical mode decomposition is the key part of HHT, while its algorithm was protected by NASA as a US patent, which limits the wide application among the scientific community. Two approaches, mirror periodic and extrema extending methods, have been developed for handling the end effects of empirical mode decomposition. The implementation of the HHT is realized in detail to widen the application. The detailed comparison of the results from two methods with that from Huang et al. (1998, 1999), and the comparison between two methods are presented. Generally, both methods reproduce faithful results as those of Huang et al. For mirror periodic method (MPM), the data are extended once forever. Ideally, it is a way for handling the end effects of the HHT, especially for the signal that has symmetric waveform. The extrema extending method (EEM) behaves as good as MPM, and it is better than MPM for the signal that has strong asymmetric waveform. However, it has to perform extrema envelope extending in every shifting process.
基金supported by National High Technology Research and Development Program of China(863 Program)(No.2015AA050104)National Natural Science Foundation of China(No.51577068)
文摘With the significant improvement of microgrid technology, microgrid has gained large-scale application.However, the existence of intermittent distributed generations, nonlinear loads and various electrical and electronic devices causes power quality problem in microgrid, especially in islanding mode. An accurate and fast disturbance detection method which is the premise of power quality control is necessary. Aiming at the end effect and the mode mixing of original Hilbert-Huang transform(HHT), an improved HHT with adaptive waveform matching extension is proposed in this paper. The innovative waveform matching extension method considers not only the depth of waveform, but also the rise time and fall time. Both simulations and field experiments have verified the correctness and validity of the improved HHT for power quality disturbance detection in microgrid.
基金Gansu Science and Technology Key Project under Grant No.2GS057-A52-008
文摘Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.
基金This project is supported by National Natural Science Foundation of China(No.50275154).
文摘Emprical mode decomposition(EMD) is a method and principle of decomposing signal dealing with Hilbert-Huang transform (HHT) in signal analysis, while directly-mean EMD is an improved EMD method presented by N.E.Huang, the inventor of HHT, which is aimed at solving the problems of EMD principle. Although the directly-mean HMD method is very remarkable with its advantages and N. E. Huang has given a method to realize it, he did not find the theoretic evidence of the method so that the feasibility of the idea and correctness of realizing the directly-mean EMD method is still indeterminate. For this a deep research on the forming process of complex signal is made and the involved stationary point principle and asymptotic stationary point principle are demonstrated, thus some theoretic evidences and the correct realizing way of directly-mean EMD method is firstly presented. Some simulation examples for demonstrating the idea presented are given.
文摘A new method is proposed to analyze multi-component linear frequency modulated （LFM） signals, which eliminates cross terms in conventional Wigner-Ville distribution （WVD）. The approach is based on Radon transform and Hilbert-Huang transform （HHT）, which is a recently developed method adaptive to non-linear and non-stationary signals. The complicated signal is decomposed into several intrinsic mode functions （IMF） by the empirical mode decomposition （EMD）, which makes the consequent instantaneous frequency meaningful. After the instantaneous frequency and Hilbert spectrum are computed, multi-component LFM signals detection and parameter estimation are obtained using Radon transform on the Hilbert spectrum plane. The simulation results show its feasibility and effectiveness.
基金Supported by National High-tech Research and Development Foundation of China (No.2001AA413210).
文摘A new method to identify flow regime in two-phase flow was presented, based on signal processing of differential pressure using Hilbert Huang transform （HHT）. Signals obtained from a Venturi meter were decomposed into different intrinsic mode functions （IMFs） with HHT, then the energy fraction of each intrinsic mode and the mean value of residual function were calculated, from which the rules of flow regime identification were summarized. Experiments were carried out on two-phase flow in the horizontal tubes with 50mm and 40mm inner diameter, while water flowrate was in the range of 1.3m^3.h^-1 to 10.5m^3.h^-1, oil flowrate was from 4.2m^3.h^-1 to 7.0m^3.h^-1 and gas flowrate from 0 to 15m^3.h^-1. The results show that the proposed rules have high precision for single phase, bubbly, and slug, plug flow regirne identification, which are independent of not only properties of two-phase fluid. In addition, the method can meet the need of industrial application because of its simple calculation.
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 w
文摘The ESMD method can be seen as a new alternate of the well-known Hilbert-Huang transform (HHT) for non-steady data processing. It is good at finding the optimal adaptive global mean fitting curve, which is superior to the common least-square method and running-mean approach. Take the air-sea momentum flux investigation as an example, only when the non-turbulent wind components is well extracted, can the remainder signal be seen as actual oscillations caused by turbulence. With the aid of —5/3 power law for the turbulence, a mode-filtering approach based on ESMD decomposition is developed here. The test on observational data indicates that this approach is very feasible and it may greatly reduce the error caused by the non-turbulent components.
文摘In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-Huang Transform to extract modal parameters for closely spaced modes and low-energy components. The proposed method is applied to a simulated airplane model built in Automatic Dynamic Analysis of Mechanical Systems software. The results demonstrate that the identified modal parameters are in good agreement with the baseline model.
文摘ABSTRACT Current computerized pulse diagnosis is mainly based on pressure and photoelectric signal. Considering the richness and complication of pulse diagnosis information, it is valuable to explore the feasibility of novel types of signal and to develop appropriate feature representation for diagnosis. In this paper, we present a study on computerized pulse diagnosis based on blood flow velocity signal. First, the blood flow velocity signal is collected using Doppler ultrasound device and preprocessed. Then, by locating the fiducial points, we extract the spatial features of blood flow velocity signal, and further present a Hilbert-Huang transform-based method for spectrum feature extraction. Finally, support vector machine is applied for computerized pulse diagnosis. Experiment results show that the proposed method is effective and promising in distinguishing healthy people from patients with cho- lecystitis or nephritis.
基金supported by the Ministry of Science and Technology of China (No. 2006DFA21650)the Institute of Earthquake Science, China Earthquake Administration (No. 0207690229)
文摘In this paper we discuss the use of the Hilbert-Huang transform（HHT） to enhance the time-frequency analysis of microtremor measurements. HHT is a powerful algorithm that combines the process of empirical mode decomposition（EMD） and the Hilbert transform to compose the HilbertHuang spectrum that contains the time-frequency-energy information of the recorded signals. HHT is an adaptive algorithm and does not require the signals to be linear or stationary. HHT is advantageous for analyzing microtremor data, since observed microtremors are commonly contaminated by nonstationary transient noises close to the recording instruments. This is especially true when microtremors are measured in an urban environment. In our data processing HHT was used to（1） eliminate the unwanted short-duration transient constituents from microtremor data and use only the coherent portion of the data to carry out the widely used horizontal to vertical spectral ratio（H/V） method;（2） identify and eliminate the continuous industrial noise in certain frequency band; and（3） enhance the H/V analysis by using the Hilbert-Huang spectrum（HHS）. The efficacy of this proposed approach is demonstrated by the examples of applying it to microtremor data acquired in the metropolitan Beijing area.
