scholarly journals The seismic signatures of the 2009 Shiaolin landslide in Taiwan

2011 ◽  
Vol 11 (5) ◽  
pp. 1559-1569 ◽  
Author(s):  
Z. Feng
Keyword(s):  
Surface Wave ◽  
Seismic Waves ◽  
Frequency Spectra ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Wave Velocities ◽  
Shear Wave Velocities ◽  
Mode Function ◽  
Mode Decomposition ◽  
Weak Surface

Abstract. The Shiaolin landslide occurred on 9 August 2009 after Typhoon Morakot struck Taiwan, claiming over 400 lives. The seismic signals produced by the landslide were recorded by broadband seismic stations in Taiwan. The time-frequency spectra for these signals were obtained by the Hilbert-Huang transform (HHT) and were analyzed to obtain the seismic characteristics of the landslide. Empirical mode decomposition (EMD) was applied to differentiate weak surface-wave signals from noise and to estimate the surface-wave velocities in the region. The surface-wave velocities were estimated using the fifth intrinsic mode function (IMF 5) obtained from the EMD. The spectra of the earthquake data were compared. The main frequency content of the seismic waves caused by the Shiaolin landslide were in the range of 0.5 to 1.5 Hz. This frequency range is smaller than the frequency ranges of other earthquakes. The spectral analysis of surface waves (SASW) method is suggested for characterizing the shear-wave velocities of the strata in the region.

Rotor Local Rubbing Fault Feature Analysis Based on Teager-Huang Transform

2014 ◽  
Vol 989-994 ◽  
pp. 3244-3247 ◽  
Author(s):  
Shang Kun Liu ◽  
Gui Ji Tang ◽  
Bin Pang
Keyword(s):  
Energy Operator ◽  
Vibration Signal ◽  
Analysis Method ◽  
Test Rig ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Teager Energy Operator ◽  
Mode Function ◽  
Mode Decomposition ◽  
Teager Energy

An analysis method based on Teager-Huang transform for rotor local rubbing fault diagnosis is introduced. Firstly, the original vibration signal is decomposed into some Intrinsic Mode Function (IMF) components by using Empirical Mode Decomposition (EMD) approach, secondly, Teager energy operator is applied to estimate the instantaneous amplitude and instantaneous frequency of each IMF component, so the time-frequency distribution of the signal is obtained. The rotor local rubbing fault is simulated on a rotor test rig. The analysis results show that this method compared with Hilbert-Huang transform (HHT) can track the occurrence of rotor local rubbing fault better and can extract the characteristics of rotor local rubbing fault effectively. It provides a reliable method for timely and accurate diagnostic to the rotor local rubbing fault.

INSTANTANEOUS FREQUENCY-BASED ANALYSIS AND CHARACTERIZATION OF LASER GENERATED DROPLET SEQUENCE DYNAMICS

2013 ◽  
Vol 05 (02) ◽  
pp. 1350008
Author(s):  
BLAŽ KRESE ◽  
EDVARD GOVEKAR
Keyword(s):  
Instantaneous Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Pulse Power ◽  
Generation Process ◽  
Frequency Spectra ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Experimental Time Series ◽  
Recent Developments

In the laser droplet generation process three different dripping regimes are experimentally observed in dependence on the detachment pulse power. Besides being nonlinear, the process is also inherently nonstationary. In order to consistently analyze all the dripping scenarios based on an experimental time series, time-frequency analysis by means of instantaneous frequency is used. For the calculation of instantaneous frequency, the most recent developments of the Hilbert–Huang transform are applied, i.e. ensemble empirical mode decomposition, empirical amplitude/frequency modulation decomposition, and direct quadrature. In time-frequency spectra specific patterns are associated with corresponding dripping regimes. By means of a detailed inspection of patterns, the influence of the detachment pulse power on dripping dynamics is characterized.

scholarly journals Common Crossing Structural Health Analysis with Track-Side Monitoring

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.

The Application of Improved HHT to Harmonic Analysis and Fault Detection of Power System

2011 ◽  
Vol 354-355 ◽  
pp. 1406-1411
Author(s):  
Wen Hua Han ◽  
Hai Xia Ren ◽  
Xu Chen ◽  
Xiao Juan Tao
Keyword(s):  
Fault Detection ◽  
Power System ◽  
Harmonic Analysis ◽  
Harmonic Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Frequency Domain Analysis ◽  
Analysis Method ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition

Hilbert-Huang transform (HHT) is a new time-frequency-domain analysis method, which is suitable for non-stationary and nonlinear signals. In this paper, endpoint continuation and ensemble empirical mode decomposition (EEMD) decomposition method are introduced to improve the HHT, which solve the endpoint winger and modal aliasing problem. The improved HHT (IHHT) is used for analyzing the harmonic signal and detecting the fault signal of power system. Simulation results show that IHHT is feasible and effective for harmonic analysis and fault detection.

Sonic logging of compressional‐wave velocities in a very slow formation

Geophysics ◽  
1995 ◽  
Vol 60 (6) ◽  
pp. 1627-1633 ◽  
Author(s):  
Bart W. Tichelaar ◽  
Klaas W. van Luik
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Compressional Wave ◽  
P Wave ◽  
Dipole Source ◽  
Frequency Spectra ◽  
Unconsolidated Sands ◽  
Wave Velocities ◽  
Shear Wave Velocities ◽  
Sonic Logging

Borehole sonic waveforms are commonly acquired to produce logs of subsurface compressional and shear wave velocities. To this purpose, modern borehole sonic tools are usually equipped with various types of acoustic sources, i.e., monopole and dipole sources. While the dipole source has been specifically developed for measuring shear wave velocities, we found that the dipole source has an advantage over the monopole source when determining compressional wave velocities in a very slow formation consisting of unconsolidated sands with a porosity of about 35% and a shear wave velocity of about 465 m/s. In this formation, the recorded compressional refracted waves suffer from interference with another wavefield component identified as a leaky P‐wave, which hampers the determination of compressional wave velocities in the sands. For the dipole source, separation of the compressional refracted wave from the recorded waveforms is accomplished through bandpass filtering since the wavefield components appear as two distinctly separate contributions to the frequency spectrum: a compressional refracted wave centered at a frequency of 6.5 kHz and a leaky P‐wave centered at 1.3 kHz. For the monopole source, the frequency spectra of the various waveform components have considerable overlap. It is therefore not obvious what passband to choose to separate the compressional refracted wave from the monopole waveforms. The compressional wave velocity obtained for the sands from the dipole compressional refracted wave is about 2150 m/s. Phase velocities obtained for the dispersive leaky P‐wave excited by the dipole source range from 1800 m/s at 1.0 kHz to 1630 m/s at 1.6 kHz. It appears that the dipole source has an advantage over the monopole source for the data recorded in this very slow formation when separating the compressional refracted wave from the recorded waveforms to determine formation compressional wave velocities.

scholarly journals On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150206 ◽  
Author(s):  
Norden E. Huang ◽  
Kun Hu ◽  
Albert C. C. Yang ◽  
Hsing-Chih Chang ◽  
Deng Jia ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
A Priori ◽  
Integral Transforms ◽  
Density Variation ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Wave Nonlinearity ◽  
Frequency Modulations ◽  
Multiplicative Processes

The Holo-Hilbert spectral analysis (HHSA) method is introduced to cure the deficiencies of traditional spectral analysis and to give a full informational representation of nonlinear and non-stationary data. It uses a nested empirical mode decomposition and Hilbert–Huang transform (HHT) approach to identify intrinsic amplitude and frequency modulations often present in nonlinear systems. Comparisons are first made with traditional spectrum analysis, which usually achieved its results through convolutional integral transforms based on additive expansions of an a priori determined basis, mostly under linear and stationary assumptions. Thus, for non-stationary processes, the best one could do historically was to use the time–frequency representations, in which the amplitude (or energy density) variation is still represented in terms of time. For nonlinear processes, the data can have both amplitude and frequency modulations (intra-mode and inter-mode) generated by two different mechanisms: linear additive or nonlinear multiplicative processes. As all existing spectral analysis methods are based on additive expansions, either a priori or adaptive, none of them could possibly represent the multiplicative processes. While the earlier adaptive HHT spectral analysis approach could accommodate the intra-wave nonlinearity quite remarkably, it remained that any inter-wave nonlinear multiplicative mechanisms that include cross-scale coupling and phase-lock modulations were left untreated. To resolve the multiplicative processes issue, additional dimensions in the spectrum result are needed to account for the variations in both the amplitude and frequency modulations simultaneously. HHSA accommodates all the processes: additive and multiplicative, intra-mode and inter-mode, stationary and non-stationary, linear and nonlinear interactions. The Holo prefix in HHSA denotes a multiple dimensional representation with both additive and multiplicative capabilities.

scholarly journals Single Frame Fringe Pattern Analysis for Phase Recovery with Analytic Signal

ISRN Optics ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-6
Author(s):  
Khalid Assid ◽  
Vamara Dembele ◽  
Faiçal Alaoui ◽  
Abdel Karim Nassim
Keyword(s):  
Fringe Pattern ◽  
Good Accuracy ◽  
Phase Distribution ◽  
Analytic Signal ◽  
One Dimensional ◽  
Hilbert Huang Transform ◽  
Mode Function ◽  
Single Frame ◽  
Mode Decomposition ◽  
Fringe Pattern Analysis

We consider a new application of the normalized Hilbert-Huang transform to extract directly the phase from a single fringe pattern. We present a technique to provide, with good accuracy, the phase distribution from a single interferogram without unwrapping step and this by a new exploitation of the analytic signal corresponding to each intrinsic mode function, resulting from one-dimensional empirical mode decomposition of the fringe pattern. A theoretical analysis was carried out for this technique, followed by computer simulations and a real experimental fringe pattern for verification.

scholarly journals ANALYSIS OF TEMPORAL VARIATIONS IN TURBIDITY FOR A COASTAL AREA USING THE HILBERT-HUANG-TRANSFORM

2011 ◽  
Vol 1 (32) ◽  
pp. 25
Author(s):  
Shigeru Kato ◽  
Magnus Larson ◽  
Takumi Okabe ◽  
Shin-ichi Aoki
Keyword(s):  
Instantaneous Frequency ◽  
River Mouth ◽  
Original Data ◽  
Temporal Variations ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
The Hilbert Transform

Turbidity data obtained by field observations off the Tenryu River mouth were analyzed using the Hilbert-Huang Transform (HHT) in order to investigate the characteristic variations in time and in the frequency domain. The Empirical Mode Decomposition (EMD) decomposed the original data into only eight intrinsic mode functions (IMFs) and a residue in the first step of the HHT. In the second step, the Hilbert transform was applied to the IMFs to calculate the Hilbert spectrum, which is the time-frequency distribution of the instantaneous frequency and energy. The changes in instantaneous frequencies showed correspondence to high turbidity events in the Hilbert spectrum. The investigation of instantaneous frequency variations can be used to understand transitions in the state of the turbidity. The comparison between the Fourier spectrum and the Hilbert spectrum integrated in time showed that the Hilbert spectrum makes it possible to detect and quantify the cycle of locally repeated events.

scholarly journals An Improvement in Representation of Audio Signal in Time-Frequency Plane using EMD-2TEMD Based Approach

2016 ◽  
Vol 44 ◽  
pp. 141-150
Author(s):  
Kazi Mahmudul Hassan ◽  
Md. Ekramul Hamid ◽  
Takayoshi Nakai
Keyword(s):  
Empirical Mode Decomposition ◽  
Source Separation ◽  
Audio Signal ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Separation Result ◽  
Mode Decomposition ◽  
Recent Approach ◽  
Non Stationary Signal

This study proposed an enhanced time-frequency representation of audio signal using EMD-2TEMD based approach. To analyze non-stationary signal like audio, timefrequency representation is an important aspect. In case of representing or analyzing such kind of signal in time-frequency-energy distribution, hilbert spectrum is a recent approach and popular way which has several advantages over other methods like STFT, WT etc. Hilbert-Huang Transform (HHT) is a prominent method consists of Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis (HSA). An enhanced method called Turning Tangent empirical mode decomposition (2T-EMD) has recently developed to overcome some limitations of classical EMD like cubic spline problems, sifting stopping condition etc. 2T-EMD based hilbert spectrum of audio signal encountered some issues due to the generation of too many IMFs in the process where EMD produces less. To mitigate those problems, a mutual implementation of 2T-EMD & classical EMD is proposed in this paper which enhances the representation of hilbert spectrum along with significant improvements in source separation result using Independent Subspace Analysis (ISA) based clustering in case of audio signals. This refinement of hilbert spectrum not only contributes to the future work of source separation problem but also many other applications in audio signal processing.

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