scholarly journals Dynamic characterizations of underwater structures using noncontact vibration tests based on nanosecond laser ablation in water: evaluation of passive vibration suppression with damping materials

2017 ◽  
Vol 24 (16) ◽  
pp. 3714-3725 ◽  
Author(s):  
Naoki Hosoya ◽  
Itsuro Kajiwara ◽  
Koh Umenai ◽  
Shingo Maeda
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Vibration Suppression ◽  
Pass Filter ◽  
Reliability Factor ◽  
Measured Frequency ◽  
Measured Frequency Response ◽  
Underwater Structure ◽  
Underwater Structures

Recently, the demand for higher performing underwater structures under diverse conditions has increased. Examples include improved precision and speed of the position control of robot manipulators. To prevent the control spillover problems when active controls are used, a control system is typically constituted with a low-pass filter to eliminate all modes except for the target modes. However, experimentally measuring the dynamic properties of an underwater structure in an environment where the structure and a fluid continuously influence each other is difficult. We have recently proposed a noncontact vibration testing method for dynamic characterizations of underwater structures in which the response to a laser ablation excitation force is measured by laser Doppler vibrometer. Integrating passive control using a vibration-damping material affixed onto the underwater structure and active control constituted with the low-pass filter may realize a more cost-effective system. To develop this combined control into a practical method, the reliability of the measured frequency response function must be validated. Additionally, the applicable frequency range must be expanded to encompass the high-frequency region (several tens of kHz) so that the vibration suppression quality of underwater structures can be evaluated. Herein we quantify the effect of random measurement errors on the measured frequency response function with a reliability factor based on the concept of coherence functions. Using the measured frequency response function with a reliability factor, we demonstrated that our method can evaluate passive vibration suppression effect of an underwater structure with a damping material in high-frequency ranges up to 20 kHz.

Damage Detection of Steel Beam Using Frequency Response Function Measurement Data and Fractal Dimension

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Eun-Taik Lee ◽  
Hee-Chang Eun
Keyword(s):  
Fractal Dimension ◽  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Measurement Data ◽  
Fractal Dimensions ◽  
Dynamic Responses ◽  
Steel Beam ◽  
Health State ◽  
Measured Frequency Response

Fractal-dimension-based signal processing has been extensively applied to various fields for nondestructive testing. The dynamic response signal can be utilized as an analytical tool to evaluate the structural health state without baseline data. The fractal features of the dynamic responses with fractal dimensions (FDs) were investigated using the Higuchi, Katz, and Sevcik methods. The waveform FD proposed by these methods was extracted from the measured frequency response function (FRF) data in the frequency domain. Damage was observed within this region, which resulted in an abrupt change in the curvature of the FD. The effectiveness of the methods was investigated via the results of a steel beam test and a numerical experiment to detect damage.

scholarly journals Frequency Domain Analysis of Tractor Tyre Enveloping Properties

2019 ◽  
Vol 4 (1) ◽  
pp. 223-229
Author(s):  
Boris Stojic
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Frequency Domain Analysis ◽  
Contact Length ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Vibration Excitation ◽  
Order Of Magnitude ◽  
Ground Profile

Main source of vibration excitation for off-road vehicles are ground profile undulations. Most unprepared terrains are characterized by wavelength of unevenness that is of the order of magnitude of the contact length between tire and ground, so that, due to its shape and elasticity, tire actually behaves as geometric low-pass filter transforming real road profile geometry into effective vehicle vibration excitation. Since this effective profile represents real vehicle excitation, it is of interest to study this filtering behaviour in more depth. In this work, investigation of this kind of tire response has been studied for agricultural tractor tire rolling quasistatically over singular road obstacle. Frequency analysis of road excitation and tyre response was carried out in order to obtain their spectra and frequency response function magnitude of the tyre as filter was obtained by dividing input by output spectra. Final assessment of frequency response function magnitude was obtained by averaging instances obtained for different dimensions of input obstacles.

Secondary Path Estimation by PCA-Compressed Frequency Response Function in Active Vibration Control

2011 ◽  
Vol 66-68 ◽  
pp. 721-726
Author(s):  
Xin Hui Li ◽  
Tie Jun Yang ◽  
Jian Chao Dong ◽  
Ze Qi Lu
Keyword(s):  
Vibration Control ◽  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Active Vibration Control ◽  
Principal Component ◽  
Good Control ◽  
Noise Elimination ◽  
Measured Frequency Response ◽  
Active Vibration

The FXLMS algorithm is widely used in active vibration control system. The estimation of secondary path plays very important roles in such a system. This paper presents an experimental investigation of effective secondary path estimation in active vibration control using measured Frequency Response Function (FRF). Principal component analysis (PCA) is pursued to the measured FRF for noise elimination, and then the PCA-compressed FRF data are used for secondary path estimation. The control results indicate that the proposed method has good control performance.

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.

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