Removal of non-stationary harmonics for operational modal analysis in time and frequency domain

2022 ◽  
Vol 165 ◽  
pp. 108329
Author(s):  
Pieter-Jan Daems ◽  
Cédric Peeters ◽  
Patrick Guillaume ◽  
Jan Helsen
Keyword(s):  
Modal Analysis ◽  
Frequency Domain ◽  
Operational Modal Analysis

Frequency-Domain System Identification Techniques for Experimental and Operational Modal Analysis

2003 ◽  
Vol 36 (16) ◽  
pp. 1609-1614 ◽  
Author(s):  
Patrick Guillaume ◽  
Peter Verboven ◽  
Bart Cauberghe ◽  
Steve Vanlanduit ◽  
Eli Parloo ◽  
...  
Keyword(s):  
System Identification ◽  
Modal Analysis ◽  
Frequency Domain ◽  
Operational Modal Analysis ◽  
Identification Techniques

scholarly journals Operational Modal Analysis of a Spar-Type Floating Platform Using Frequency Domain Decomposition Method

Energies ◽  
2016 ◽  
Vol 9 (11) ◽  
pp. 870 ◽  
Author(s):  
Carlo Ruzzo ◽  
Giuseppe Failla ◽  
Maurizio Collu ◽  
Vincenzo Nava ◽  
Vincenzo Fiamma ◽  
...  
Keyword(s):  
Domain Decomposition ◽  
Modal Analysis ◽  
Frequency Domain ◽  
Decomposition Method ◽  
Domain Decomposition Method ◽  
Operational Modal Analysis ◽  
Floating Platform ◽  
Frequency Domain Decomposition

scholarly journals Transmissibilty-Based Operational Modal Analysis for Flight Flutter Testing Using Exogenous Inputs

2012 ◽  
Vol 19 (5) ◽  
pp. 1071-1083 ◽  
Author(s):  
Christof Devriendt ◽  
Tim De Troyer ◽  
Gert De Sitter ◽  
Patrick Guillaume
Keyword(s):  
Modal Analysis ◽  
Frequency Domain ◽  
Experimental Modal Analysis ◽  
Operational Modal Analysis ◽  
Modal Parameters ◽  
Input Output ◽  
Output Only ◽  
Flutter Testing

During the recent years several new tools have been introduced by the Vrije Universiteit Brussel in the field of Operational Modal Analysis (OMA) such as the transmissibility based approach and the the frequency-domain OMAX concept. One advantage of the transmissibility based approach is that the ambient forces may be coloured (non-white), if they are fully correlated. The main advantage of the OMAX concept is the fact that it combines the advantages of Operational and Experimental Modal Analysis: ambient (unknown) forces as well as artificial (known) forces are processed simultaneously resulting in improved modal parameters. In this paper, the transmissibility based output-only approach is combined with the input/output OMAX concept. This results in a new methodology in the field of operational modal analysis allowing the estimation of (scaled) modal parameters in the presence of arbitrary ambient (unknown) forces and artificial (known) forces.

scholarly journals Operational Modal Analysis of Damage Gear

2020 ◽  
Vol 9 (3) ◽  
pp. 2778-2783
Keyword(s):  
Domain Decomposition ◽  
Modal Analysis ◽  
Frequency Domain ◽  
Dynamic Characteristics ◽  
Natural Frequencies ◽  
Helical Gear ◽  
Operational Modal Analysis ◽  
Mode Shapes ◽  
Identification Algorithm ◽  
Frequency Domain Decomposition

As natural frequencies and mode shapes are often a key to understanding dynamic characteristics of structural elements, modal analysis provides a viable means to determine these properties. This paper investigates the dynamic characteristics of a healthy and unhealthy condition of a commercially used helical gear using the Frequency Domain Decomposition (FDD) identification algorithm in Operational Modal Analysis (OMA). For the unhealthy condition, a refined range of percentage of defects are introduced to the helical gear starting from one (1) tooth being defected (1/60 teeth) to six (6) teeth being defected (6/60 teeth). The specimen is tested under a free-free boundary condition for its simplicity and direct investigation purpose. Comparison of the results of these varying conditions of the structure will be shown to justify the validity of the method used. Acceptable modal data are obtained by considering and accentuating on the technical aspects in processing the experimental data which are critical aspects to be addressed. The natural frequencies and mode shapes are obtained through automatic and manual peak-picking process from Singular Value Decomposition (SVD) plot using Frequency Domain Decomposition (FDD) technique and the results are validated using the established Modal Assurance Criterion (MAC) indicator. The results indicate that OMA using FDD algorithm is a good method in identifying the dynamic characteristics and hence, is effective in detection of defects in this rotating element

Operational modal analysis of Canton Tower by a fast frequency domain Bayesian method

2016 ◽  
Vol 17 (2) ◽  
pp. 209-230 ◽  
Author(s):  
Feng-Liang Zhang ◽  
Yi-Qing Ni ◽  
Yan-Chun Ni ◽  
You-Wu Wang
Keyword(s):  
Modal Analysis ◽  
Frequency Domain ◽  
Bayesian Method ◽  
Operational Modal Analysis

Combining Cyclostationarity and the Frequency Domain Subspace Technique for Operational Modal Analysis

2007 ◽  
Vol 347 ◽  
pp. 473-478
Author(s):  
Jerome Antoni ◽  
David Hanson ◽  
Bob Randall
Keyword(s):  
Modal Analysis ◽  
Frequency Domain ◽  
Combustion Engine ◽  
Operational Modal Analysis ◽  
Mode Shapes ◽  
Response Measurement ◽  
Underlying Assumption ◽  
Input Multiple Output ◽  
Identification Technique ◽  
Output System

An underlying assumption of many operational modal analysis techniques is that the excitation is evenly distributed over the system, i.e. the inputs are spatially white, and is constant with frequency, i.e. frequentially white. This paper investigates the use of cyclostationarity, in combination with the Frequency Domain Subspace identification technique, to relax these constraints. Such a technique is suitable for application on systems which are excited by at least one cyclostationary input with a unique cyclic frequency, such as an internal combustion engine in a car or locomotive. The cyclostationary properties of this input are employed to reduce a multiple-inputmultiple- output system to a single-input-multiple-output system by extracting the component of each response measurement which is attributable to the cyclostationary input alone. The system modal properties; the resonances, damping and mode shapes, are then identified using the frequency domain subspace algorithm. The effectiveness of the technique is demonstrated through experiments on a laboratory test rig and a passenger train, and compared with results obtained using the knowledge of the inputs.

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