سال انتشار: ۱۳۹۱

محل انتشار: دومین کنفرانس بین المللی آکوستیک و ارتعاشات

تعداد صفحات: ۸

نویسنده(ها):

Mohammad Hassan Masjedian – Department of Mechanical Engineering, Isfahan University of Technology,8415683111,
Mehdi Keshmiri –

چکیده:

Operational Modal Analysis (OMA) is an efficient and useful technique for extracting modal parameters of structures. In OMA only the response to the ambient forces in normal working conditions are used. Due to the assumption of stochastic input forces, OMA methods have many limitations and difficulties in the presence of harmonic excitations. Many researchers adapted OMA methods to consider presence of harmonic excitations. Nevertheless, in a ro-tary machine, several powerful harmonic excitations superimposed to the stochastic forces and most of the OMA methods will fail in that case. In this paper, the Curve-Fitted Enhanced Frequency Domain Decomposition (CFDD) method is used for OMA of rotating machiner-ies. CFDD method is a robust technique to harmonic excitation in OMA. In this method, mo-dal parameters are estimated using curve-fitting in frequency domain. An estimation of SDOF frequency response function is used to extract modal parameters via curve-fitting in full frequency band and the harmonic components are removed by linear interpolation in SVD graph. Using the entire frequency band to form regression problem causes extra compu-tation. Also using linear interpolation may cause error in extracted modal parameters espe-cially if a harmonic peak coincides with a structural natural frequency. In this paper modified CFDD method is presented by two modifications. The first modification is using limited data in the vicinity of each mode to form regression problem. The second modification is to elimi-nate the frequency lines corresponding to harmonic components instead of linear interpola-tion. The applicability of the new method is evaluated by modal parameter extraction of a large industrial fan in Mobarakeh Steel Complex using the response of fan in its normal op-eration.