Evolutionary Spectral Analyses of a Powerful Typhoon at the Sutong Bridge Site Based on the HHT

Mathematical Problems in Engineering ◽

10.1155/2016/4143715 ◽

2016 ◽

Vol 2016 ◽

pp. 1-18

Author(s):

Lin Ma ◽

Da-jun Zhou ◽

Ai-min Yuan ◽

Hao Wang

Keyword(s):

Power Spectrum ◽

Wind Velocity ◽

Spectral Characteristics ◽

Velocity Spectrum ◽

Spectral Analyses ◽

Hilbert Huang Transform ◽

Evolutionary Spectrum ◽

Mode Decomposition ◽

Sutong Bridge ◽

Averaged Power Spectrum

To investigate the nonstationary characteristics of strong typhoons, this paper considers the evolutionary spectral characteristics of strong typhoons based on the Hilbert-Huang transform (HHT). Discrete expressions are determined for the evolutionary spectral analysis based on the HHT. The study indicates that the classic empirical mode decomposition (EMD) method fails to extract all of the high-frequency fluctuations from wind velocity data, and the time-averaged power spectrum obtained directly using the HHT cannot provide the true wind velocity spectrum. The degrees of nonstationarity of different-order IMF components are analysed and a synthesized method of analysing the evolutionary spectrum and time-averaged power spectrum of a strong typhoon is proposed. To avoid the energy leakage problem that exists in HHT spectral analyses, the Gram-Schmidt method is used to orthogonalize the intrinsic mode function (IMF) components. The study indicates that when the orthogonalization is implemented in accordance with the sequence from high-order IMF components to low-order ones, the orthogonalized components retain the same good Hilbert property as that of the IMFs. The synthesized method proposed yields a time-averaged power spectrum that is consistent with the Fourier spectrum in value and can produce the energy distributions of a typhoon in the time and frequency domains simultaneously.

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DETECTION AND QUANTIFICATION OF CONTROL VALVE NONLINEARITIES USING HILBERT–HUANG TRANSFORM

Advances in Adaptive Data Analysis ◽

10.1142/s179353690900014x ◽

2009 ◽

Vol 01 (03) ◽

pp. 425-446 ◽

Cited By ~ 10

Author(s):

S. BABJI ◽

P. GORAI ◽

A. K. TANGIRALA

Keyword(s):

Power Spectrum ◽

Fourier Transforms ◽

Control Valve ◽

Wave Frequency ◽

Intrinsic Mode Functions ◽

Research Activity ◽

Control Loops ◽

Hilbert Huang Transform ◽

Mode Decomposition ◽

Valve Stiction

Two of the most important sources of degradation of control loop performance are (i) valve stiction and (ii) tight controller tuning, both of which lead to oscillations in closed–loop outputs. A factor that distinguishes these two sources is the nonlinear signature of the valve stiction; a tightly tuned controller produces oscillations due to a linear source. Detection and isolation of nonlinear fault sources is essential to correctly determine the cause of poor loop performance of control loops. Despite a rich research activity in this area, there is hardly a method which can isolate the simultaneous effects of these two sources. Moreover, the traditional spectral analysis based on Fourier Transforms is largely restricted by the assumption of stationarity in the data to detect and quantify valve nonlinearities. In this work, Hilbert–Huang Transform (HHT) is used to (i) detect valve nonlinearities and (ii) isolate linear and nonlinear fault sources. The key characteristic of HHT is that it represents nonlinearities as intra-wave frequency modulations allowing it to distinguish it from linearities which do not exhibit such modulations. The advantages of HHT-based methods are that (i) nonlinearities translate to a unique signature (ii) nonstationarities in data can be handled in a natural way. It is observed that nonlinearity is captured by a Intrinsic Mode Functions (IMF) obtained from the Empirical Mode Decomposition (EMD) of the process output. The Hilbert–Huang spectrum of these IMFs exhibits intra-wave frequency modulation. The power spectrum of the IMFs shows the presence of harmonics which is used to characterize the valve stiction nonlinearity. Subsequent to detection, quantification is done using the power spectrum of the IMFs. The proposed method is sensitive enough to detect low levels of valve stiction nonlinearities. Results from simulation using one-parameter valve stiction model are presented in support of the proposed methodology. The results demonstrate the advantage and potential of the HHT-based method.

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Turbulence coherence and its impact on wind-farm power fluctuations

Journal of Fluid Mechanics ◽

10.1017/jfm.2018.713 ◽

2018 ◽

Vol 855 ◽

pp. 1116-1129 ◽

Cited By ~ 8

Author(s):

Nicolas Tobin ◽

Leonardo P. Chamorro

Keyword(s):

Power Spectrum ◽

Atmospheric Turbulence ◽

Turbulent Diffusion ◽

Wind Farm ◽

Spectral Characteristics ◽

Wind Farms ◽

High Frequencies ◽

Power Fluctuations ◽

The Impact ◽

Coherence Spectrum

Using a physics-based approach, we infer the impact of the coherence of atmospheric turbulence on the power fluctuations of wind farms. Application of the random-sweeping hypothesis reveals correlations characterized by advection and turbulent diffusion of coherent motions. Those contribute to local peaks and troughs in the power spectrum of the combined units at frequencies corresponding to the advection time between turbines, which diminish in magnitude at high frequencies. Experimental inspection supports the results from the random-sweeping hypothesis in predicting spectral characteristics, although the magnitude of the coherence spectrum appears to be over-predicted. This deviation is attributed to the presence of turbine wakes, and appears to be a function of the turbulence approaching the first turbine in a pair.

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Common Crossing Structural Health Analysis with Track-Side Monitoring

Communications - Scientific letters of the University of Zilina ◽

10.26552/com.c.2019.3.77-84 ◽

2019 ◽

Vol 21 (3) ◽

pp. 77-84

Author(s):

Mykola Sysyn ◽

Olga Nabochenko ◽

Franziska Kluge ◽

Vitalii Kovalchuk ◽

Andriy Pentsak

Keyword(s):

Gaussian Process Regression ◽

Ensemble Empirical Mode Decomposition ◽

Frequency Resolution ◽

Hilbert Huang Transform ◽

Time Frequency ◽

Structural Health ◽

Rolling Surface ◽

Mode Decomposition ◽

Inertial Measurements ◽

Common Crossing

Track-side inertial measurements on common crossings are the object of the present study. The paper deals with the problem of measurement's interpretation for the estimation of the crossing structural health. The problem is manifested by the weak relation of measured acceleration components and impact lateral distribution to the lifecycle of common crossing rolling surface. The popular signal processing and machine learning methods are explored to solve the problem. The Hilbert-Huang Transform (HHT) method is used to extract the time-frequency features of acceleration components. The method is based on Ensemble Empirical Mode Decomposition (EEMD) that is advantageous to the conventional spectral analysis methods with higher frequency resolution and managing nonstationary nonlinear signals. Linear regression and Gaussian Process Regression are used to fuse the extracted features in one structural health (SH) indicator and study its relation to the crossing lifetime. The results have shown the significant relation of the derived with GPR indicator to the lifetime.

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Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-13-00238.1 ◽

2014 ◽

Vol 31 (9) ◽

pp. 1982-1994 ◽

Cited By ~ 2

Author(s):

Xiaoying Chen ◽

Aiguo Song ◽

Jianqing Li ◽

Yimin Zhu ◽

Xuejin Sun ◽

...

Keyword(s):

Empirical Mode Decomposition ◽

Recognition Rate ◽

Texture Feature ◽

Intrinsic Mode Functions ◽

Hilbert Spectrum ◽

Feature Extraction Method ◽

Hilbert Huang Transform ◽

Mode Decomposition ◽

Marginal Spectrum ◽

Bidimensional Empirical Mode Decomposition

Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

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Complex empirical mode decomposition, Hilbert-Huang transform, and fourier transform applied to moving objects

2012 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2012.6350399 ◽

2012 ◽

Author(s):

Kristen Wallis ◽

Geoffrey Akers ◽

Peter Collins ◽

Richard Davis ◽

Alan Frazier ◽

...

Keyword(s):

Fourier Transform ◽

Empirical Mode Decomposition ◽

Moving Objects ◽

Hilbert Huang Transform ◽

Mode Decomposition

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Signal Analysis Through the Ensemble Empirical Mode Decomposition and Hilbert-Huang Transform-Application to Vortex Shedding

Mechanisms and Machine Science - Vibration Engineering and Technology of Machinery ◽

10.1007/978-3-030-60694-7_6 ◽

2021 ◽

pp. 95-120

Author(s):

Ana Paula Ost ◽

Alexandre Vagtinski de Paula ◽

Sergio Viçosa Möller

Keyword(s):

Empirical Mode Decomposition ◽

Vortex Shedding ◽

Signal Analysis ◽

Ensemble Empirical Mode Decomposition ◽

Hilbert Huang Transform ◽

Mode Decomposition

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Numerical Simulation of Coherent Structures in the Turbulent Boundary Layer under Different Stability Conditions

Energies ◽

10.3390/en13051068 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1068

Author(s):

Shujin Laima ◽

Hehe Ren ◽

Hui Li ◽

Jinping Ou

Keyword(s):

Boundary Layer ◽

Turbulent Boundary Layer ◽

Correlation Coefficient ◽

Wind Velocity ◽

Coherent Structures ◽

Stability Conditions ◽

Velocity Spectrum ◽

Turbulent Eddy ◽

Stable Conditions ◽

The Stability

Coherent structures in the turbulent boundary layer were investigated under different stability conditions. Qualitative analyses of the flow field, spatial correlation coefficient field and pre-multiplied wind velocity spectrum showed that the dominant turbulent eddy structure changed from small-scale motions to large- and very-large-scale motions and then to thermal plumes as the stability changed from strong stable to neutral and then to strong unstable. A quantitative analysis of the size characteristics of the three-dimensional turbulent eddy structure based on the spatial correlation coefficient field showed that under near-neutral stability, the streamwise, wall-normal and spanwise extents remained constant at approximately 0.3 δ , 0.1 δ and 0.2 δ ( δ , boundary layer height), respectively, while for other conditions, the extent in each direction varied in a log-linear manner with stability; only the spanwise extent under stable conditions was also independent of stability. The peak wavenumber of the pre-multiplied wind velocity spectrum moves towards small values from stable conditions to neutral condition and then to unstable conditions; thus, for the wind velocity spectrum, another form is needed that takes account the effects of the stability condition.

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HHT based analysis on seismic response recordings for a base-isolated building

MATEC Web of Conferences ◽

10.1051/matecconf/201927702021 ◽

2019 ◽

Vol 277 ◽

pp. 02021

Author(s):

Fei Wang ◽

Xiandong Kang ◽

Ting Yan ◽

Ying Liu

Keyword(s):

Seismic Response ◽

Dominant Frequency ◽

Data Series ◽

Structural Systems ◽

Original Signal ◽

Intrinsic Mode Functions ◽

Hilbert Huang Transform ◽

Mode Decomposition ◽

Frequency Components ◽

Emd Method

Hilbert-Huang transform (HHT) is proposed to process the seismic response recordings in an 8-story frame-shear wall base-isolated building. Empirical Mode Decomposition (EMD) method is first applied to identify the time variant characteristics and the data series can be decomposed into several components. Hilbert transform is well-behaved in identifying the frequency components. The first 5 intrinsic mode functions (IMFs) are decomposed with their different frequencies. The analytical function is reconstructed and compared with the original signal. They are extremely consistent in amplitude and phase. Based on the IMFs obtained, frequencies of the original signal are inferred at 5 Hz and 1.6 Hz. The higher frequency is regarded as the vibration excited by surface waves. 1.6 Hz is suggested as the dominant frequency of the building. Analysis indicates that HHT is accurate in extracting the dynamic characteristics of structural systems.

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