scholarly journals Transformer winding modal parameter identification based on poly-reference least-square complex frequency domain method

2016 ◽  
Author(s):  
Yaning Li ◽  
Hong Yu ◽  
Xiaoyan Zhu
Keyword(s):  
Parameter Identification ◽  
Frequency Domain ◽  
Least Square ◽  
Frequency Domain Method ◽  
Complex Frequency ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Domain Method ◽  
Transformer Winding ◽  
Square Complex

scholarly journals The PolyMAX Frequency-Domain Method: A New Standard for Modal Parameter Estimation?

2004 ◽  
Vol 11 (3-4) ◽  
pp. 395-409 ◽  
Author(s):  
Bart Peeters ◽  
Herman Van der Auweraer ◽  
Patrick Guillaume ◽  
Jan Leuridan
Keyword(s):  
Parameter Estimation ◽  
Frequency Response ◽  
Least Squares ◽  
Frequency Domain ◽  
Real Life ◽  
Mode Shapes ◽  
Frequency Domain Method ◽  
Complex Frequency ◽  
Modal Parameter ◽  
Domain Method

Recently, a new non-iterative frequency-domain parameter estimation method was proposed. It is based on a (weighted) least-squares approach and uses multiple-input-multiple-output frequency response functions as primary data. This so-called “PolyMAX” or polyreference least-squares complex frequency-domain method can be implemented in a very similar way as the industry standard polyreference (time-domain) least-squares complex exponential method: in a first step a stabilisation diagram is constructed containing frequency, damping and participation information. Next, the mode shapes are found in a second least-squares step, based on the user selection of stable poles. One of the specific advantages of the technique lies in the very stable identification of the system poles and participation factors as a function of the specified system order, leading to easy-to-interpret stabilisation diagrams. This implies a potential for automating the method and to apply it to “difficult” estimation cases such as high-order and/or highly damped systems with large modal overlap. Some real-life automotive and aerospace case studies are discussed. PolyMAX is compared with classical methods concerning stability, accuracy of the estimated modal parameters and quality of the frequency response function synthesis.

Finite-Difference Complex-Frequency-Domain Method for Optical and Plasmonic Analyses

2018 ◽  
Vol 30 (11) ◽  
pp. 1024-1027 ◽  
Author(s):  
Di Wu ◽  
R. Ohnishi ◽  
R. Uemura ◽  
T. Yamaguchi ◽  
S. Ohnuki
Keyword(s):  
Finite Difference ◽  
Frequency Domain ◽  
Frequency Domain Method ◽  
Complex Frequency ◽  
Domain Method

System Eigenvalue Identification of Mistuned Bladed Disks Using Least-Squares Complex Frequency-Domain Method

2017 ◽  
Author(s):  
Yuan Huang ◽  
Grigorios Dimitriadis ◽  
Robert E. Kielb ◽  
Jing Li
Keyword(s):  
Least Squares ◽  
Frequency Domain ◽  
Research Effort ◽  
Response Prediction ◽  
Forced Response ◽  
Frequency Domain Method ◽  
Common Denominator ◽  
Complex Frequency ◽  
Modal Parameter ◽  
Stabilization Diagram

This paper presents the results from a research effort on eigenvalue identification of mistuned bladed rotor systems using the Least-Squares Complex Frequency-Domain (LSCF) modal parameter estimator. The LSCF models the frequency response function (FRF) obtained from a vibration test using a matrix-fraction description and obtains the coefficients of the common denominator polynomial by minimizing the least squares error of the fit between the FRF and the model. System frequency and damping information is obtained from the roots of the denominator; a stabilization diagram is used to separate physical from mathematical poles. The LSCF estimator is known for its good performance when separating closely spaced modes, but few quantitative analyses have focused on the sensitivity of the identification with respect to mode concentration. In this study, the LSCF estimator is applied on both computational and experimental forced responses of an embedded compressor rotor in a three-stage axial research compressor. The LSCF estimator is first applied to computational FRF data obtained from a mistuned first-torsion (1T) forced response prediction using FMM (Fundamental Mistuning Model) and is shown to be able to identify the eigenvalues with high accuracy. Then the first chordwise bending (1CWB) computational FRF data is considered with varied mode concentration by varying the mistuning standard deviation. These cases are analyzed using LSCF and a sensitivity algorithm is developed to evaluate the influence of the mode spacing on eigenvalue identification. Finally, the experimental FRF data from this rotor blisk is analyzed using the LSCF estimator. For the dominant modes, the identified frequency and damping values compare well with the computational values.

A Polyreference Least Square Complex Frequency domain based statistical test for damage detection

2008 ◽  
Vol 41 (2) ◽  
pp. 4511-4516 ◽  
Author(s):  
Gilles Canales ◽  
Laurent Mevel ◽  
Michle Basseville
Keyword(s):  
Damage Detection ◽  
Frequency Domain ◽  
Statistical Test ◽  
Least Square ◽  
Complex Frequency ◽  
Square Complex

Frequency Domain Modal Parameter Identification of High Order Systems in a Numerically Stable Way

1997 ◽  
Vol 119 (2) ◽  
pp. 265-270 ◽  
Author(s):  
K. Q. Xu
Keyword(s):  
Parameter Identification ◽  
Frequency Domain ◽  
Narrow Band ◽  
Wide Frequency Range ◽  
Frequency Range ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Identification Methods ◽  
The Time Domain ◽  
Experimental Data Analysis

Frequency domain modal parameter identification methods have several attractive properties as compared with the time domain methods except for the limitation of low-order-and-narrow-band per analysis. As rule of thumb, a limit of less than ten modes has been observed for several popular frequency domain algorithms. However, this paper will show, that with a proper and thorough use of the orthogonal polynomials in the frequency domain, the number of modes per analysis can be increased to as high as 75 in a comparatively wide frequency range of interest while still retaining numerical stability. Both numerical example (75 modes in 5–1000 Hz) and experimental data analysis (56 modes in 50–5000 Hz) are presented to demonstrate the effectiveness of this innovative approach.

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