Feasibility of Describing Joint Nonlinearity in Exhaust Components With Modal Iwan Models

Author(s):  
Daniel R. Roettgen ◽  
Matthew S. Allen ◽  
Dan Osgood ◽  
Stuart Gerger

Segalman recently proposed a model for joint nonlinearity in a built up structure in which each mode is treated independently (orthogonality is assumed to be preserved) and with an Iwan model added to each modal degree of freedom to capture the nonlinearity of all of the joints that are active in that mode. Recent works have shown that this type of model can faithfully describe the nonlinearity in simple laboratory structures and in simulations of structures with several Iwan joints in the micro-slip regime. This work explores the validity of these concepts for more complicated structures, each of which is part of a production automotive exhaust system. Where possible, factory gaskets were used and the bolted joints were tightened per the manufacturer’s specifications. Tests were performed on different subassemblies of the exhaust using a modal hammer to excite the structure and accelerometers to measure its response. Mayes & Allen’s ZEFFT algorithm was used to determine which modes were behaving nonlinearly. Then an algorithm based on the Hilbert transform was used to extract the instantaneous frequency and damping for the modes of interest and to fit the behavior to a modal Iwan model. The results show several modes that exhibit small frequency shifts and damping that changes by as much as a factor of two over the range of forces that were employed.

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>


Author(s):  
Michael Feldman

This paper describes a new technique, called the Hilbert Vibration Decomposition method, dedicated to decomposition of non-stationary wideband dynamic signals. Using the Hilbert transform in the time domain, we extract a number of elementary oscillating components of the initial signal, who’s both the instantaneous frequency and envelope can vary in time. Modeling examples of decomposition of non-stationary signals are included.


Author(s):  
Joaqui´n Ortega ◽  
George H. Smith

The Hilbert-Huang Transform (HHT) was proposed by Huang et al. [2] as a method for the analysis of non-linear, non-stationary time series. This procedure requires the decomposition of the signal into intrinsic mode functions using a method called empirical mode decomposition. These functions represent the essential oscillatory modes contained in the original signal. Their characteristics ensure that a meaningful instantaneous frequency is obtained through the application of the Hilbert Transform. The Hilbert Transform is applied to each intrinsic mode function and the amplitude and instantaneous frequency for every time-step is computed. The resulting representation of the energy in terms of time and frequency is defined as the Hilbert Spectrum. In previous work [7] using the HHT for the analysis of storm waves it has been observed that the number of IMFs needed for the decomposition and the amount of energy associated to different IMFs differ from what has been observed for the analysis of waves under ‘normal’ sea conditions by other authors. In this work we explore in detail the effect that the sampling rate has in the empirical mode decomposition and in the Hilbert Spectrum for storm waves. The results show that the amount of energy associated to different IMFs varies with the sampling rate and also that the number of IMFs needed for the empirical mode decomposition changes with record length.


2009 ◽  
Vol 01 (02) ◽  
pp. 177-229 ◽  
Author(s):  
NORDEN E. HUANG ◽  
ZHAOHUA WU ◽  
STEVEN R. LONG ◽  
KENNETH C. ARNOLD ◽  
XIANYAO CHEN ◽  
...  

Instantaneous frequency (IF) is necessary for understanding the detailed mechanisms for nonlinear and nonstationary processes. Historically, IF was computed from analytic signal (AS) through the Hilbert transform. This paper offers an overview of the difficulties involved in using AS, and two new methods to overcome the difficulties for computing IF. The first approach is to compute the quadrature (defined here as a simple 90° shift of phase angle) directly. The second approach is designated as the normalized Hilbert transform (NHT), which consists of applying the Hilbert transform to the empirically determined FM signals. Additionally, we have also introduced alternative methods to compute local frequency, the generalized zero-crossing (GZC), and the teager energy operator (TEO) methods. Through careful comparisons, we found that the NHT and direct quadrature gave the best overall performance. While the TEO method is the most localized, it is limited to data from linear processes, the GZC method is the most robust and accurate although limited to the mean frequency over a quarter wavelength of temporal resolution. With these results, we believe most of the problems associated with the IF determination are resolved, and a true time–frequency analysis is thus taking another step toward maturity.


2020 ◽  
Vol 2020 (48) ◽  
pp. 17-24
Author(s):  
I.M. Javorskyj ◽  
◽  
R.M. Yuzefovych ◽  
P.R. Kurapov ◽  
◽  
...  

The correlation and spectral properties of a multicomponent narrowband periodical non-stationary random signal (PNRS) and its Hilbert transformation are considered. It is shown that multicomponent narrowband PNRS differ from the monocomponent signal. This difference is caused by correlation of the quadratures for the different carrier harmonics. Such features of the analytic signal must be taken into account when we use the Hilbert transform for the analysis of real time series.


Author(s):  
Jiapeng Liu ◽  
Ting Hei Wan ◽  
Francesco Ciucci

<p>Electrochemical impedance spectroscopy (EIS) is one of the most widely used experimental tools in electrochemistry and has applications ranging from energy storage and power generation to medicine. Considering the broad applicability of the EIS technique, it is critical to validate the EIS data against the Hilbert transform (HT) or, equivalently, the Kramers–Kronig relations. These mathematical relations allow one to assess the self-consistency of obtained spectra. However, the use of validation tests is still uncommon. In the present article, we aim at bridging this gap by reformulating the HT under a Bayesian framework. In particular, we developed the Bayesian Hilbert transform (BHT) method that interprets the HT probabilistic. Leveraging the BHT, we proposed several scores that provide quick metrics for the evaluation of the EIS data quality.<br></p>


Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 65
Author(s):  
Benjamin Akers ◽  
Tony Liu ◽  
Jonah Reeger

A radial basis function-finite differencing (RBF-FD) scheme was applied to the initial value problem of the Benjamin–Ono equation. The Benjamin–Ono equation has traveling wave solutions with algebraic decay and a nonlocal pseudo-differential operator, the Hilbert transform. When posed on R, the former makes Fourier collocation a poor discretization choice; the latter is challenging for any local method. We develop an RBF-FD approximation of the Hilbert transform, and discuss the challenges of implementing this and other pseudo-differential operators on unstructured grids. Numerical examples, simulation costs, convergence rates, and generalizations of this method are all discussed.


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