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

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.

New Algorithm to Identify Inrush Current Based on Improved EMD

2012 ◽  
Vol 433-440 ◽  
pp. 6927-6934
Author(s):  
Da Zhuang Chen ◽  
Jia Dong Huang ◽  
Yang Sun
Keyword(s):  
Data Driven ◽  
Power Transformers ◽  
Inrush Current ◽  
Analysis Method ◽  
Hilbert Huang Transform ◽  
The Core ◽  
Mode Decomposition ◽  
Multicomponent Signals ◽  
Frequency Components ◽  
Emd Method

Empirical mode decomposition (EMD), which is the core mechanic of the Hilbert-Huang transform(HHT), is a local, fully data driven and self-adaptive analysis approach. It is a powerful tool for analyzing multi-component signals. Aiming at the reduction of scale mixing and artificial frequency components, an improved scheme was proposed for analysis and reconstruction of nonstationary and multicomponent signals. The improved EMD method uses the wavelet analysis method and normalized correlation coefficient to deal with the problems. Because the inrush current is a peaked wave with nonstationary component, a new algorithm based on improved EMD is presented for fast discrimination between inrush current and fault current of power transformers. Theoretical analysis and dynamic simulation results show that the method is effective and reliable under various fault conditions and simple to be applied.

NOISE-MODULATED EMPIRICAL MODE DECOMPOSITION

2010 ◽  
Vol 02 (01) ◽  
pp. 25-37 ◽  
Author(s):  
PO-HSIANG TSUI ◽  
CHIEN-CHENG CHANG ◽  
NORDEN E. HUANG
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
The Core ◽  
Mode Decomposition ◽  
Threshold Amplitude ◽  
The Mean ◽  
Mixing Problem ◽  
Emd Method

The empirical mode decomposition (EMD) is the core of the Hilbert–Huang transform (HHT). In HHT, the EMD is responsible for decomposing a signal into intrinsic mode functions (IMFs) for calculating the instantaneous frequency and eventually the Hilbert spectrum. The EMD method as originally proposed, however, has an annoying mode mixing problem caused by the signal intermittency, making the physical interpretation of each IMF component unclear. To resolve this problem, the ensemble EMD (EEMD) was subsequently developed. Unlike the conventional EMD, the EEMD defines the true IMF components as the mean of an ensemble of trials, each consisting of the signal with added white noise of finite, not infinitesimal, amplitude. In this study, we further proposed an extension and alternative to EEMD designated as the noise-modulated EMD (NEMD). NEMD does not eliminate mode but intensify and amplify mixing by suppressing the small amplitude signal but the larger signals would be preserved without waveform deformation. Thus, NEMD may serve as a new adaptive threshold amplitude filtering. The principle, algorithm, simulations, and applications are presented in this paper. Some limitations and additional considerations of using the NEMD are also discussed.

Application of the Hilbert-Huang Transform and Enhanced Noise Filtering to the Damage Detection of Structural Systems

2007 ◽  
Author(s):  
Jeng-Wen Lin ◽  
Hung-Jen Chen ◽  
Chong-Shien Tsai
Keyword(s):  
Damage Detection ◽  
Structural Systems ◽  
Noise Filtering ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Empirical Mode Decomposition Method ◽  
Filtering Technique ◽  
Effective Design

This paper proposes an enhanced noise filtering technique to the Hilbert-Huang transformation technology for the damage detection of structural systems using vibration measurements. The Hilbert-Huang transformation technology is a set of superior algorithms for analyzing non-linear and non-stationary signals and also for linear and stationary signals. However, the signals are filtered by reconstruction from “selected” intrinsic mode functions (IMFs), derived from the original signal through the empirical mode decomposition method. The proposed filtering technique offers the criterion for selecting the IMFs using the orthogonalization coefficient. Through the enhanced filtering, it is possible to increase the accuracy for the estimation of the time-varying system’s natural frequency, indicative of the degree of damage, which helps an effective design of a control system.

scholarly journals Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3510 ◽  
Author(s):  
Zhijian Wang ◽  
Junyuan Wang ◽  
Wenhua Du
Keyword(s):  
Fault Diagnosis ◽  
Signal To Noise Ratio ◽  
Optimal Number ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Multiple Fault ◽  
Mode Decomposition ◽  
Fault Features ◽  
Reconstructed Signal

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.

scholarly journals Output-Only Modal Analysis Based on Improved Empirical Mode Decomposition Method

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Shiqiang Qin ◽  
Qiuping Wang ◽  
Juntao Kang
Keyword(s):  
Modal Analysis ◽  
Empirical Mode Decomposition ◽  
Modal Parameters ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Random Decrement Technique ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Output Only ◽  
Emd Method

The output-only modal analysis for bridge structures based on improved empirical mode decomposition (EMD) is investigated in this study. First, a bandwidth restricted EMD is proposed for decomposing nonstationary output measurements with close frequency components. The advantage of bandwidth restricted EMD to standard EMD is illustrated by a numerical simulation. Next, the modal parameters are extracted from intrinsic mode function obtained from the improved EMD by both random decrement technique and stochastic subspace identification. Finally, output-only modal analysis of a railway bridge is presented. The study demonstrates the mode mixing issues of standard EMD can be restrained by introducing bandwidth restricted signal. Further, with the improved EMD method, band-pass filter is no longer needed for separating the closely spaced frequency components. The modal parameters extracted based on the improved EMD method show good agreement with those extracted by conventional modal identification algorithms.

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

2014 ◽  
Vol 31 (9) ◽  
pp. 1982-1994 ◽  
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.

scholarly journals Assessment of Lift Passenger Comfort by the Hilbert–Huang Transform

2019 ◽  
Vol 8 (2) ◽  
pp. 373-380 ◽  
Author(s):  
Kamil Szydło ◽  
Piotr Wolszczak ◽  
Rafał Longwic ◽  
Grzegorz Litak ◽  
Mieczysław Dziubiński ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Human Body ◽  
Dominant Frequency ◽  
Health Condition ◽  
Passenger Comfort ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Resonance Frequencies ◽  
As Stress ◽  
The Hilbert Transform

Abstract Purpose The comfort of lift passengers has a significant effect on their general health condition as well as stress levels during travel. This study reports the results of vibration measurements taken during travel in a passenger lift. Methods Vibration signals were analyzed by the empirical mode decomposition method and the Hilbert transform. Results Selected modes from the Hilbert spectral analysis were compared with the resonance frequencies of human body organs (range 20–90 Hz) as well as with the resonance frequencies of lift components. Conclusion The use of Hilbert spectral analysis enables the isolation of individual signal components and the determination of the dominant frequency in the signal. This, in turn, allows for the isolation of raw vibration frequencies from the signal that are particularly significant for passenger comfort assessment (resonance frequencies of human body organs) and analysis of their occurrence.

WEIGHTED SLIDING EMPIRICAL MODE DECOMPOSITION

2011 ◽  
Vol 03 (04) ◽  
pp. 509-526 ◽  
Author(s):  
R. FALTERMEIER ◽  
A. ZEILER ◽  
A. M. TOMÉ ◽  
A. BRAWANSKI ◽  
E. W. LANG
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Sampling Rate ◽  
Computational Effort ◽  
Intrinsic Mode Functions ◽  
Weighted Version ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Reconstruction Quality ◽  
Spectral Transform

The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely data-driven manner. EMD adaptively and locally decomposes such time series into a sum of oscillatory modes, called Intrinsic mode functions (IMF) and a nonstationary component called residuum. In this contribution, we propose an EMD-based method, called Sliding empirical mode decomposition (SEMD), which, with a reasonable computational effort, extends the application area of EMD to a true on-line analysis of time series comprising a huge amount of data if recorded with a high sampling rate. Using nonlinear and nonstationary toy data, we demonstrate the good performance of the proposed algorithm. We also show that the new method extracts component signals that fulfill all criteria of an IMF very well and that it exhibits excellent reconstruction quality. The method itself will be refined further by a weighted version, called weighted sliding empirical mode decomposition (wSEMD), which reduces the computational effort even more while preserving the reconstruction quality.

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

2009 ◽  
Vol 01 (03) ◽  
pp. 425-446 ◽  
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.

scholarly journals Time-Frequency Characteristics Analysis on Vibration Signals of a Three-Supported Rotor System with Misalignment

2011 ◽  
Vol 2-3 ◽  
pp. 717-721 ◽  
Author(s):  
Xiao Xuan Qi ◽  
Mei Ling Wang ◽  
Li Jing Lin ◽  
Jian Wei Ji ◽  
Qing Kai Han
Keyword(s):  
Wavelet Packet ◽  
Vibration Signal ◽  
Dominant Frequency ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Characteristics Analysis ◽  
Novel Method ◽  
Misalignment Fault

In light of the complex and non-stationary characteristics of misalignment vibration signal, this paper proposed a novel method to analyze in time-frequency domain under different working conditions. Firstly, decompose raw misalignment signal into different frequency bands by wavelet packet (WP) and reconstruct it in accordance with the band energy to remove noises. Secondly, employ empirical mode decomposition (EMD) to the reconstructed signal to obtain a certain number of stationary intrinsic mode functions (IMF). Finally, apply further spectrum analysis on the interested IMFs. In this way, weak signal is caught and dominant frequency is picked up for the diagnosis of misalignment fault. Experimental results show that the proposed method is able to detect misalignment fault characteristic frequency effectively.

Sign in / Sign up

Export Citation Format

Share Document