Abstract
Abstract In recent years, modal analysis has become one of the essential methods for modification and optimization of dynamic characteristics of engineering structures. This is the first published study to identify modal parameters of a complex four-stage centrifugal compressor using Operational Modal Analysis (OMA). Vibrational response was measured continuously with sampling frequency of 44100(Hz) by four noncontact eddy current sensors. Applied loads in actual working condition during compressor’s operation were considered as excitation forces. In this study, modal parameters were extracted and compared using various OMA methods, including Frequency Domain Decomposition (FDD), Enhanced Frequency Domain Decomposition (EFDD) and Stochastic Subspace Identification (SSI). PULSETM commercial software as well as an in-house MATLAB code employed to data analysis. The results show that SSI method has a higher accuracy compared to FDD and EFDD methods. However, FDD shows better results when system damping is low in one of the modes.
Highlights
Experimental Modal Analysis EMA is an empirical technique used to determine the modal model of a linear time invariant vibrational system
In 2008, Reynders and Roeck used a combination of ran‐ dom and determined methods for experimental and operational modal analysis and evaluated the performance of this method using data gathered from a bridge
This study aims to investigate the performance of common operational modal analysis algorithms for struc‐ tural modal identification of a complex four‐stage centrifugal compressor in presence of harmonic excitations
Summary
Experimental Modal Analysis EMA is an empirical technique used to determine the modal model of a linear time invariant vibrational system. It is not possible to apply EMA to determine modal parameters These structures are effectively excited by forces created by their own operation which are impossible to measure. Another work by the same authors proposed a bias removal method based on stability diagram and variance error estimation using first order sensitivity of modal parameters determined by stochastic subspace method. They verified the accuracy and performance of this. In 2000, Brincker et al proposed a criterion for identification and separation of structure’s real modes from its har‐ monic modes for the first time This criterion is based on fundamental difference between statistical characteris‐ tics of harmonic and random responses in a narrow band near structural modes Brincker et al, 2000a . The data was fed to some developed codes in MATLAB and the results were compared to each other
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