Output-only modal identification by in-operation modal appropriation for use with enhanced frequency domain decomposition method

Output-only modal identification or operational modal analysis (OMA) has gained increasing popularity in many fields of engineering. In the context of OMA there is no need to measure the input. Modal parameters of dynamic systems are estimated just based on the output responses. One of the most robust and popular frequency domain methods of OMA is enhanced frequency domain decomposition (EFDD) method. EFDD is widely used as an interesting solution for OMA in a large number of researches, projects, and commercial software. We first assessed the EFDD features considering the design parameters selection and then introduced our recently proposed algorithm named in-operation modal appropriation (INOPMA) for use with EFDD method to improve the identification of modal frequencies and damping ratios and overcome some of the existing drawbacks. Modal identification process starts with EFDD and then INOPMA is applied on derived normalized auto-correlation functions (NACF) to estimate modal damping ratios and natural frequencies. Typical EFDD takes advantage of logarithmic decrement (LD) algorithm and zero crossing (ZC) method at this stage. A simulated four-story shear frame has been employed to perform the evaluation of the proposed method on output-only prediction of ideal natural frequencies and modal damping ratios. The outcomes show that the modal parameters are estimated with much less bias error and variance compared to the typical EFDD procedure. The real data of the heritage court tower ambient vibration test is also used to perform sensitivity analysis of EFDD-INOPMA modal parameter estimation in the presence of measurement noise, and favorable results have been obtained.

[1]  Gaëtan Kerschen,et al.  Output-only modal analysis using blind source separation techniques , 2007 .

[2]  Jong Wan Hu,et al.  Unsupervised identification of arbitrarily-damped structures using time-scale independent component analysis: Part I , 2018 .

[3]  David J. Ewins,et al.  Modal Testing: Theory, Practice, And Application , 2000 .

[4]  S. El-Borgi,et al.  A Modal Filtering and Statistical Approach for Damage Detection and Diagnosis in Structures using Ambient Vibrations Measurements , 2007 .

[5]  Daniel J. Inman,et al.  Modal Appropriation for Use with In-Operation Modal Analysis , 2015 .

[6]  Tong Wang,et al.  A frequency–spatial domain decomposition (FSDD) method for operational modal analysis , 2010 .

[7]  Jie Guo,et al.  Output-only modal identification based on hierarchical Hough transform , 2016 .

[8]  Rune Brincker,et al.  Improvement of Frequency Domain Output Only Modal Identification from the Application of the Random Decrement Technique , 2004 .

[9]  Palle Andersen,et al.  Applications of Frequency Domain Curve-fitting in the EFDD Technique , 2008 .

[10]  Carlos E. Ventura,et al.  Damping estimation by frequency domain decomposition , 2001 .

[11]  Christof Devriendt,et al.  The use of transmissibility measurements in output-only modal analysis , 2007 .

[12]  Egidio Rizzi,et al.  Output-only modal dynamic identification of frames by a refined FDD algorithm at seismic input and high damping , 2016 .

[13]  Palle Andersen,et al.  Modal Identification from Ambient Responses using Frequency Domain Decomposition , 2000 .

[14]  Egidio Rizzi,et al.  Refined Frequency Domain Decomposition modal dynamic identification from earthquake-induced structural responses , 2017 .

[15]  Thomas G. Carne,et al.  The Natural Excitation Technique (NExT) for modal parameter extraction from operating wind turbines , 1993 .

[16]  Luca Martinelli,et al.  Damage detection and localization on a benchmark cable-stayed bridge , 2015 .

[17]  Johan Schoukens,et al.  A comprehensive study of the bias and variance of frequency-response-function measurements: Optimal window selection and overlapping strategies , 2007, Autom..

[18]  Ahmad Shooshtari,et al.  OPERATIONAL MODAL ANALYSIS TECHNIQUES AND THEIR THEORETICAL AND PRACTICAL ASPECTS :A COMPREHENSIVE REVIEW AND INTRODUCTION , 2015 .

[19]  Leonard Meirovitch,et al.  Elements Of Vibration Analysis , 1986 .

[20]  Weui-Bong Jeong,et al.  Sensor placement optimization for structural modal identification of flexible structures using genetic algorithm , 2015 .

[21]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[22]  Michèle Basseville,et al.  Subspace-based fault detection algorithms for vibration monitoring , 2000, Autom..

[23]  Carlos E. Ventura,et al.  Introduction to Operational Modal Analysis: Brincker/Introduction to Operational Modal Analysis , 2015 .

[24]  Bart De Moor,et al.  Subspace algorithms for the stochastic identification problem, , 1993, Autom..

[25]  Ahmad Shooshtari,et al.  A Comparative Assessment of In-Operation Modal Analysis and Frequency Domain Decomposition Algorithm Using Simulated Data , 2017 .

[26]  Carlos E. Ventura,et al.  Introduction to Operational Modal Analysis: Brincker/Introduction to Operational Modal Analysis , 2015 .

[27]  D. Giraldo,et al.  Modal Identification through Ambient Vibration: Comparative Study , 2009 .