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 . PULSE TM 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.
In recent years, structures made of Functionally Graded materials (FGMs) are used in industries due to the continuously compositional variation of the constituents in FGMs along different directions. In order to develop FGMs, nonlinear vibration analysis to study dynamic behavior is needed. This study proposes nonlinear vibration analysis of a simply supported axially functionally graded (AFG) beam subjected to a moving harmonic load as an Euler-Bernoulli beam utilizing Green’s strain tensor. Axial variation of material properties of the beam is based on the power law. The governing equations of motion are derived via Hamilton’s principle. The Galerkin’s method is implemented to reduce the nonlinear partial differential equations of the system to a number of nonlinear ordinary differential equations. He’s variational method is applied to obtain approximate analytical expressions for the nonlinear frequency and the nonlinear dynamic response of the AFG beam. The effect of some parameters such as the power index and stiffness coefficients, among others, on the nonlinear natural frequency has been investigated. The influence of above mentioned parameters as well as the velocity of the moving harmonic load on the nonlinear dynamic response has been studied. The results indicate that these parameters have a considerable effect on both nonlinear natural frequency and response amplitude.
Fast methods to identification and recognition of structural defects are important issues for the industry. In recent years, a growing interest has been on quick structural inspection to significantly reduce inspection's cost and time, while minimizing the number of Non-Destructive Testing (NDT). Investigations on damage detection methods based on vibration monitoring have been developed in the last decade. This paper presents a study on Inner Product Vector (IPV) method. The IPV method is a new vibration based damage detection technique. In this method vibration responses are measured before and after damage occurrence. The vibration responses include the time domain acceleration (or displacement or velocity). The IPV method has the potential to significantly reduce costs by minimizing the need for NDT methods. For damage detection via the IPV method, a threshold should be selected for classifying the damaged and undamaged sections of a structure. Proper determination of the threshold value requires selection of Confidence Interval Factor (CIF). In this study, a new algorithm for the IPV method is suggested in which a new optimized model for damage detection is presented. Aforementioned optimized model can provide an accurate value for the CIF. To ascertain the exact CIF, the damage detection method is simulated. An accurate threshold makes the IPV method capable to accurately detect damages. The method has been verified by means of an FEM model as well as an experimental case study. The results show that the optimization process can be used as a reference to ascertain value for the CIF. The IPV method can be used as a Structural Health Monitoring (SHM) method, but it's necessary to optimize the IPV method for each sample.
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