Identification of MDOF Non-Linear Uncoupled Dynamical Systems Using Hilbert Transform and Empirical Mode Decomposition Method

2011 ◽  
Vol 255-260 ◽  
pp. 1676-1680
Author(s):  
Tian Li Huang ◽  
Wei Xin Ren ◽  
Meng Lin Lou

A non-linear dynamical system identification method using Hilbert transform (HT) and empirical mode decomposition (EMD) is proposed. For a single-degree-of-freedom (SDOF) nonlinear system, the Hilbert transform identification method is good at identifying the instantaneous modal parameters (natural frequencies, damping characteristics and their dependencies on a vibration amplitude and frequency). For the multi-degree-of-freedom (MDOF) non-linear uncoupled dynamical systems, the EMD method is attempting for the decomposition of response signals into a collection of mono-components signals, termed intrinsic mode functions (IMFs). Considering the IMFs admit a well-behaved Hilbert transform, the HT identification method has been applied for the identification of nonlinear properties. The numerical simulation of a 2-dof shear-beam building model with nonlinear stiffness illustrated the proposed technique.

2020 ◽  
Author(s):  
Sébastien Wouters ◽  
Michel Crucifix ◽  
Matthias Sinnesael ◽  
Anne-Christine Da Silva ◽  
Christian Zeeden ◽  
...  

<p>Cyclostratigraphy is increasingly used to improve the Geologic Time Scale and our understanding of past climatic systems. However, except in a few existing methodologies, the quality of the results is often not evaluated.</p><p>We propose a new methodology to document this quality, through a decomposition of a signal into a set of narrow band components from which instantaneous frequency and amplitude can be computed, using the Hilbert transform. The components can be obtained by Empirical Mode Decomposition (EMD), but also by filtering a signal (be it tuned or not) in any relevant way, and by subsequently performing EMD on the signal minus its filtered parts.</p><p>From that decomposition, verification is performed to estimate the pertinence of the results, based on different concepts that we introduce:</p><ul><li> Integrity quantifies to what extent the sum of the components is equal to the signal. It is defined as the cumulated difference between (1) the signal, and (2) the summed components of the decomposition. EMD fulfils integrity by design, except for errors caused by floating-decimal arithmetic. Ensemble Empirical Mode Decomposition (EEMD) may fail to satisfy integrity unless noisy realisations are carefully chosen in the algorithm to cancel each other when averaging the realisations. We present such an algorithm implemented in R: “extricate”, which performs EEMD in a few seconds.</li> <li> Parsimony checks that the decomposition does not generate components that heavily cancel out. We propose to quantify it as the ratio between (1) the cumulated absolute values of each component (except the trend), and (2) the cumulated absolute values of the signal (minus the trend). The trend should be ignored in the calculation, because an added trend decreases the parsimony estimation of a similar decomposition.</li> <li> IMF departure (IMFD) quantifies the departure of each component to the definition of intrinsic mode functions (IMF), from which instantaneous frequency can reliably be computed. We define it as the mean of the absolute differences of the base 2 logarithms of frequencies obtained using (1) a robust generalized zero-crossing method (GZC, which simplifies the components into extrema separated by zero-crossings) and (2) a more local method such as the Hilbert Transform.</li> <li> Reversibility is the concept that all initial data points are preserved, even after linear interpolation and tuning. This allows to revert back to the original signal and discuss the significance of each data point. To facilitate reversibility we introduce the concept of quanta (smallest depth or time interval having significance for a given sampling) and an algorithm computing the highest rational common divisor of given values in R: “divisor”.</li> </ul><p>This new methodology allows to check the final result of cyclostratigraphic analysis independently of how it was performed (i.e. a posteriori). Once the above-mentioned concepts are taken into account, the instantaneous frequencies, ratios of frequencies and amplitudes of the components can be computed and used to interpret the pertinence of the analysis in a geologically meaningful way. The instantaneity and independence of frequency and amplitude so obtained open a new way of performing time-series analysis.</p>


2006 ◽  
Vol 74 (2) ◽  
pp. 223-230 ◽  
Author(s):  
Z. Y. Shi ◽  
S. S. Law

This paper addresses the identification of linear time-varying multi-degrees-of-freedom systems. The identification approach is based on the Hilbert transform and the empirical mode decomposition method with free vibration response signals. Three-different types of time-varying systems, i.e., smoothly varying, periodically varying, and abruptly varying stiffness and damping of a linear time-varying system, are studied. Numerical simulations demonstrate the effectiveness and accuracy of the proposed method with single- and multi-degrees-of-freedom dynamical systems.


Author(s):  
Fulun Yang ◽  
Chin An Tan ◽  
Frank Chen

This paper investigates the identification of mechanisms of disc brake squeal by the application of a recently developed Empirical Mode Decomposition method (EMD). A known strength of the EMD is its adaptive nature in analyzing nonstationary data, with success in its original application to ocean mechanics. The EMD decomposes an original signal into a number of intrinsic mode functions (IMFs), with each IMF often containing distinct physical significance. Several sets of disc brake squeal data were obtained and processed by EMD. A typical set data is presented in this paper for discussion. Employing a sifting process in the EMD, four prominent squeal-related IMFs are identified in this set of data. The Hilbert transform is then used to analyze the frequency and amplitude contents of the four IMFs, and it is shown that the first IMF is dominant. The spectrogram method is applied to analyze the time-evolution of the frequency components of the IMFs in the squeal process. This analysis procedure confirms an important squeal mechanism, i.e., the squeal condition is governed by the coupling of in-plane and out-of-plane vibration modes of the rotor and the coalescence of their natural frequencies. The inverse approach outlined in this paper is shown to be useful for providing new insights and confirming established hypotheses of disc brake squeal.


2019 ◽  
Vol 34 (01) ◽  
Author(s):  
Kapil Choudhary ◽  
Girish Kumar Jha ◽  
Rajeev Ranjan Kumar

Agricultural commodities prices depends on production, unnecessary demand, production uncertainty, market flaws etc. Due to these factors agricultural price series are non-stationary and non-linear in nature. Therefore analyzing agricultural commodities prices is considered as a challenging task. The traditional stationary approach of time series is unable to capture non-stationary and non-linear properties of agricultural price series. Non-stationary and non-linear properties present in the price series may be accurately analyzed through empirical mode decongation (EMD). In this technique, the original time series decomposed into intrinsic mode functions and residue. One of the major limitation of EMD is the presence of the mode mixing. To overcome this limitation of the EMD, we use ensemble empirical mode decomposition (EEMD). Using this technique in this study, Delhi market potato prices have been analyzed.


2014 ◽  
Vol 926-930 ◽  
pp. 1800-1805 ◽  
Author(s):  
Guo Dong Han ◽  
Shu Ting Wan ◽  
Zhan Jie Lv ◽  
Rong Hai Liu ◽  
Jin Wang ◽  
...  

This paper puts forward a kind of gearbox fault diagnosis methods which based on empirical mode decomposition (EMD), Hilbert transform, Fast Fourier Transform (FFT) and spectrum refined techniques. This method is applicable to the analysis of the nonlinear unsteady signal. First of all used wavelet denoising to the acquisition of gearbox vibrate signal, again carries on the empirical mode decomposition (EMD), than get a certain number of intrinsic mode function (imf); Choose the specific imf, based on kurtosis value, after the Hilbert transform and Fast Fourier Transform is done, the corresponding power spectrum can be obtained; To refine the power spectrum and extract the gearbox fault characteristic frequency; Then in pattern recognition and diagnosis of the gearbox fault, and compared with the normal signal characteristics. The analysis results show that the proposed method can effectively detect the gearbox fault characteristics.


2010 ◽  
Vol 02 (03) ◽  
pp. 337-358 ◽  
Author(s):  
ROLAND PABEL ◽  
ROBIN KOCH ◽  
GABRIELA JAGER ◽  
ANGELA KUNOTH

The Hilbert–Huang-Transform (HHT) has proven to be an appropriate multiscale analysis technique specifically for nonlinear and nonstationary time series on non-equidistant grids. It is empirically adapted to the data: first, an additive decomposition of the data (empirical mode decomposition, EMD) into certain multiscale components is computed, denoted as intrinsic mode functions. Second, to each of these components, the Hilbert transform is applied. The resulting Hilbert spectrum of the modes provides a localized time-frequency spectrum and instantaneous (time-dependent) frequencies. For the first step, the empirical decomposition of the data, a different method based on local means has been developed by Chen et al. (2006). In this paper, we extend their method to multivariate data sets in arbitrary space dimensions. We place special emphasis on deriving a method which is numerically fast also in higher dimensions. Our method works in a coarse-to-fine fashion and is based on adaptive (tensor-product) spline-wavelets. We provide some numerical comparisons to a method based on linear finite elements and one based on thin-plate-splines to demonstrate the performance of our method, both with respect to the quality of the approximation as well as the numerical efficiency. Second, for a generalization of the Hilbert transform to the multivariate case, we consider the Riesz transformation and an embedding into Clifford-algebra valued functions, from which instantaneous amplitudes, phases and orientations can be derived. We conclude with some numerical examples.


2010 ◽  
Vol 02 (04) ◽  
pp. 429-449 ◽  
Author(s):  
CHIH-YUAN TSENG ◽  
HC LEE

The Hilbert-Huang transform (HHT) method, which is designed to analyze nonstationary and nonlinear time-dependent data, is attracting lots of attention. The HHT first applies the empirical mode decomposition (EMD) to decompose data into intrinsic mode functions (IMF). The Hilbert transform then is applied to the IMFs to reveal its instantaneous frequency spectrum. However, because the EMD lacks analytical interpretation, the meaning of IMFs is unclear. This work proposes an entropic analysis strategy to provide an information-based interpretation. Based on this strategy, three applications in data analysis are demonstrated: (1) studies of characteristic of white noise, (2) determination of minimum sampling rates to generate sufficient numbers of realizations, and (3) a low pass noise filter design.


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