Joint structural parameter identification using a subset of frequency response function measurements

1993 ◽  
Vol 7 (6) ◽  
pp. 509-530 ◽  
Author(s):  
Kyung-Taek Yang ◽  
Youn-sik Park
Keyword(s):  
Parameter Identification ◽  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Structural Parameter

Frequency response function-based parameter identification from short data sequences

2004 ◽  
Vol 18 (5) ◽  
pp. 1097-1116 ◽  
Author(s):  
B. Cauberghe ◽  
P. Guillaume ◽  
P. Verboven ◽  
S. Vanlanduit ◽  
E. Parloo
Keyword(s):  
Parameter Identification ◽  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function

scholarly journals Parameter Identification of Weakly Nonlinear Vibration System in Frequency Domain

2004 ◽  
Vol 11 (5-6) ◽  
pp. 685-692 ◽  
Author(s):  
Jiehua Peng ◽  
Jiashi Tang ◽  
Zili Chen
Keyword(s):  
Parameter Identification ◽  
Frequency Response ◽  
Nonlinear Vibration ◽  
Frequency Domain ◽  
Response Function ◽  
Frequency Response Function ◽  
Physical Parameters ◽  
Vibration System ◽  
Weakly Nonlinear ◽  
Nonlinear Vibration System

A new method of identifying parameters of nonlinearly vibrating system in frequency domain is presented in this paper. The problems of parameter identification of the nonlinear dynamic system with nonlinear elastic force or nonlinear damping force are discussed. In the method, the mathematic model of parameter identification is frequency response function. Firstly, by means of perturbation method the frequency response function of weakly nonlinear vibration system is derived. Next, a parameter transformation is made and the frequency response function becomes a linear function of the new parameters. Then, based on this function and with the least square method, physical parameters of the system are identified. Finally, the applicability of the proposed technique is confirmed by numerical simulation.

Modal Testing and Parameter Identification of Active Magnetic Bearing System

1995 ◽  
Author(s):  
Chong-Won Lee ◽  
Young-Ho Ha ◽  
Cheol-Soon Kim ◽  
Chee-Young Joh
Keyword(s):  
Parameter Identification ◽  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Magnetic Bearing ◽  
Experimental Results ◽  
Active Magnetic Bearing ◽  
Modal Testing ◽  
Modal Parameters ◽  
Bearing System

Abstract Complex modal testing is employed for parameter identification of a four-axis active magnetic bearing system. In the test, magnetic bearings are used as exciters while the system is in operation. The experimental results show that the directional frequency response function, which is properly defined in the complex domain, is a powerful tool for identification of bearing as well as modal parameters.

The Improved Frequency Response Function and its Effect on Modal Circle Fits

1984 ◽  
Vol 51 (3) ◽  
pp. 657-663 ◽  
Author(s):  
K. B. Elliott ◽  
L. D. Mitchell
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
System Analysis ◽  
Resonance Region ◽  
Random Excitation ◽  
Structural Parameter ◽  
Parameter Estimates ◽  
Average Percentage ◽  
Fitting Error

When structures are excited by random force excitation the circle fits of the data around resonance are usually poor. The structural parameter estimates, which result from this fit, are usually erroneous. No matter how elegant the circle or the multimodal fit, the results will be poor if the frequency response function (FRF) is a poor representation of the actual structural response. In general for the random excitation case, this is the case. The conventional fast Fourier transform (FFT) method which is used to estimate the frequency response function, is given by H1 (f) = Gxy/Gxx. This produces poor results when the coherence of the data falls in the resonance region. A drop in the coherence usually indicates noise at the input of the structure for this case. H1 (f) is quite sensitive to such noise giving erroneous estimates. This paper investigates an alternative method for computing the frequency response function, H2 (f) = Gyy/Gyx, and its impact on the accuracy of the circle fit procedure used in modal analysis. This new estimator is not sensitive to input noise like the currently used H1 (f). H2(f) provides the best estimate at or around resonance even in the presence of noise on the input signal. If one defines the average percentage fit error in the circle fit operation as 100 times the average radial deviation of the data points from the radius of the statistically fit circle divided by the fit circle radius, one can compute the circle fit accuracy for each of the proposed methods of data treatment. Typically, the percentage fitting error for H1 (f) might be 10 percent while the fitting error for H2 (f) using exactly the same data will be 0.5 percent. Thus, the proposed method eliminates long-standing system analysis errors through the use of a simple revision of the way the data are treated in the FFT processor around the resonance regions.

scholarly journals Non-linear joint parameter identification using the frequency response function of the linear substructure

2004 ◽  
Vol 218 (9) ◽  
pp. 947-955 ◽  
Author(s):  
W-J Kim ◽  
B-Y Lee ◽  
Y-S Park
Keyword(s):  
Parameter Identification ◽  
Frequency Response ◽  
Frequency Domain ◽  
Response Function ◽  
Frequency Response Function ◽  
Excitation Amplitude ◽  
The Finite Element Method ◽  
Unknown Parameters ◽  
Identification Process ◽  
Non Linear

A method based on frequency domain approaches is presented for the non-linear parameter identification of a structure having non-linear joints. The frequency response function (FRF) of the linear substructure, which can be calculated from the finite element method or measured by an experimental method, is used to calculate its FRFs needed in the parameter identification process. This method is easily applicable to a complex real structure having non-linear joints since it uses the FRF of the substructure. Since this method is performed in the frequency domain, the number of equations can be easily increased to as many as required to identify unknown parameters, not only by just varying the excitation amplitude but also by selecting the excitation frequencies. The validity of this method was tested numerically and experimentally with a cantilever beam having a non-linear element. It was verified through examples that the proposed method is useful to identify the non-linear joint parameters of a structure having arbitrary boundaries.

scholarly journals Road Roughness Estimation Based on the Vehicle Frequency Response Function

Actuators ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 89
Author(s):  
Qingxia Zhang ◽  
Jilin Hou ◽  
Zhongdong Duan ◽  
Łukasz Jankowski ◽  
Xiaoyang Hu
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Gaussian White Noise ◽  
Estimation Method ◽  
Time Shift ◽  
Ride Quality ◽  
The Road ◽  
Road Roughness ◽  
Measured Response

Road roughness is an important factor in road network maintenance and ride quality. This paper proposes a road-roughness estimation method using the frequency response function (FRF) of a vehicle. First, based on the motion equation of the vehicle and the time shift property of the Fourier transform, the vehicle FRF with respect to the displacements of vehicle–road contact points, which describes the relationship between the measured response and road roughness, is deduced and simplified. The key to road roughness estimation is the vehicle FRF, which can be estimated directly using the measured response and the designed shape of the road based on the least-squares method. To eliminate the singular data in the estimated FRF, the shape function method was employed to improve the local curve of the FRF. Moreover, the road roughness can be estimated online by combining the estimated roughness in the overlapping time periods. Finally, a half-car model was used to numerically validate the proposed methods of road roughness estimation. Driving tests of a vehicle passing over a known-sized hump were designed to estimate the vehicle FRF, and the simulated vehicle accelerations were taken as the measured responses considering a 5% Gaussian white noise. Based on the directly estimated vehicle FRF and updated FRF, the road roughness estimation, which considers the influence of the sensors and quantity of measured data at different vehicle speeds, is discussed and compared. The results show that road roughness can be estimated using the proposed method with acceptable accuracy and robustness.

scholarly journals Predicting Dam Flood Discharge Induced Ground Vibration with Modified Frequency Response Function

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 144
Author(s):  
Yan Zhang ◽  
Jijian Lian ◽  
Songhui Li ◽  
Yanbing Zhao ◽  
Guoxin Zhang ◽  
...  
Keyword(s):  
Frequency Response ◽  
Frequency Distribution ◽  
Response Function ◽  
Frequency Response Function ◽  
Ground Vibration ◽  
Vibration Source ◽  
Ground Vibrations ◽  
High Energies ◽  
Flood Discharge ◽  
Opening Method

Ground vibrations induced by large flood discharge from a dam can damage surrounding buildings and impact the quality of life of local residents. If ground vibrations could be predicted during flood discharge, the ground vibration intensity could be mitigated by controlling or tuning the discharge conditions by, for example, changing the flow rate, changing the opening method of the orifice, and changing the upstream or downstream water level, thereby effectively preventing damage. This study proposes a prediction method with a modified frequency response function (FRF) and applies it to the in situ measured data of Xiangjiaba Dam. A multiple averaged power spectrum FRF (MP-FRF) is derived by analyzing four major factors when the FRF is used: noise, system nonlinearity, spectral leakages, and signal latency. The effects of the two types of vibration source as input are quantified. The impact of noise on the predicted amplitude is corrected based on the characteristics of the measured signal. The proposed method involves four steps: signal denoising, MP-FRF estimation, vibration prediction, and noise correction. The results show that when the vibration source and ground vibrations are broadband signals and two or more bands with relative high energies, the frequency distribution of ground vibration can be predicted with MP-FRF by filtering both the input and output. The amplitude prediction loss caused by filtering can be corrected by adding a constructed white noise signal to the prediction result. Compared with using the signal at multiple vibration sources after superimposed as input, using the main source as input improves the accuracy of the predicted frequency distribution. The proposed method can predict the dominant frequency and the frequency bands with relative high energies of the ground vibration downstream of Xiangjiaba Dam. The predicted amplitude error is 9.26%.

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