scholarly journals NON-LINEAR FREE VIBRATION IDENTIFICATION VIA THE HILBERT TRANSFORM

1997 ◽  
Vol 208 (3) ◽  
pp. 475-489 ◽  
Author(s):  
M. Feldman
Keyword(s):  
Free Vibration ◽  
Hilbert Transform ◽  
Non Linear ◽  
The Hilbert Transform

Non-Linear Spring and Damping Forces Estimation During Free Vibration

1995 ◽  
Author(s):  
Michael Feldman ◽  
Simon Braun
Keyword(s):  
Free Vibration ◽  
Hilbert Transform ◽  
Time Domain ◽  
Free Vibration Analysis ◽  
Single Degree Of Freedom ◽  
Non Linear ◽  
Linear Spring ◽  
The Time Domain ◽  
The Hilbert Transform ◽  
Vibrating Systems

Abstract A method for dynamic analysis of sophisticated nonlinear single-degree-of-freedom systems, based on the Hilbert transform in the time domain is described. Using the Hilbert transform together with the proposed method for system identification, we obtain both instantaneous modal parameters together with non-linear force characteristics during free vibration analysis under impulse excitation without long resonance testing. Using the Hilbert transform in the time domain is a new method of studying linear and non-linear vibrating systems exposed to impulse or shock inputs.

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
Keyword(s):  
Dynamical Systems ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Degree Of Freedom ◽  
Intrinsic Mode Functions ◽  
Nonlinear Properties ◽  
Identification Method ◽  
Mode Decomposition ◽  
Non Linear ◽  
The Hilbert Transform

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.

Time-Varying and Non-Linear Dynamical System Identification Using the Hilbert Transform

2005 ◽  
Author(s):  
Michael Feldman
Keyword(s):  
Hilbert Transform ◽  
Special Kind ◽  
Harmonic Signal ◽  
Dynamic Structure ◽  
Forced Vibrations ◽  
Time Varying ◽  
Transform Method ◽  
Non Linear ◽  
Force Signal ◽  
The Hilbert Transform

The objective of the paper is to explain a modern Hilbert transform method for analysis and identification of mechanical non-linear vibration structures in the case of quasiperiodic signals. This special kind of periodicity arises in experimental vibration signals. The method is based on the Hilbert transform of input and output signals in a time domain to extract the instantaneous dynamic structure characteristics. The paper focuses on the dynamic analysis and identification of three groups of dynamics systems: • Forced vibrations of linear and non-linear SDOF systems excited with quasiperiodic force signal. • Combined forced vibrations of quasiperiodic time varying linear and non-linear SDOF systems excited with harmonic signal. • Combined self-excited and forced vibrations of non-linear SDOF systems excited with harmonic signal. The study focuses on signal processing techniques for nonlinear system investigation, which enable us to estimate instantaneous system dynamic parameters (natural frequencies, damping characteristics and their dependencies on a vibration amplitude and frequency) for different kinds of system excitation.

Non-Parametric Identification of Asymmetric Signals and Characterization of a Class of Non-Linear Systems Based on Frequency Modulation

2016 ◽  
Author(s):  
Václav Ondra ◽  
Ibrahim A. Sever ◽  
Christoph W. Schwingshackl
Keyword(s):  
Linear Systems ◽  
Hilbert Transform ◽  
Frequency Modulation ◽  
Parametric Identification ◽  
Free Decay ◽  
Non Linear ◽  
Non Linear Systems ◽  
The Hilbert Transform ◽  
Non Parametric

Non-parametric and parametric identification of a non-linear system is often performed by estimating instantaneous amplitude and frequency using the Hilbert transform. However, the Hilbert transform cannot be used for the accurate analysis of asymmetric signals and the reliable estimation of intra-wave frequency modulation. This paper proposes two alternatives to the Hilbert transform which not only avoid some of its mathematical and numerical issues, but also allow the above mentioned analyses. The first method, based on zero-crossing, allows the backbone and damping curves as well as the elastic and damping force characteristics of an asymmetric free decay to be identified. The application and accuracy of this method are demonstrated on the free decay of the system with off-centre clearance. The second method, based on direct quadrature, estimates intrawave frequency modulation frequency with sufficient resolution for characterization of non-linear systems which have stiffness non-linearities. The use of this method is shown on a system with cubic hardening stiffness.

Model of multicomponent narrow-band periodical non-stationary random signal

2020 ◽  
Vol 2020 (48) ◽  
pp. 17-24
Author(s):  
I.M. Javorskyj ◽  
◽  
R.M. Yuzefovych ◽  
P.R. Kurapov ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Hilbert Transform ◽  
Spectral Properties ◽  
Narrow Band ◽  
Analytic Signal ◽  
Random Signal ◽  
Hilbert Transformation ◽  
The Hilbert Transform

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.

A Bayesian View on the Hilbert Transform and the Kramers-Kronig Transform of Electrochemical Impedance Data: Probabilistic Estimates and Quality Scores

2020 ◽  
Author(s):  
Jiapeng Liu ◽  
Ting Hei Wan ◽  
Francesco Ciucci
Keyword(s):  
Power Generation ◽  
Electrochemical Impedance Spectroscopy ◽  
Hilbert Transform ◽  
Electrochemical Impedance ◽  
The Self ◽  
Impedance Data ◽  
The Hilbert Transform ◽  
Validation Tests ◽  
Self Consistency ◽  
Mathematical Relations

<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>

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