Substructural Response Reconstruction in Wavelet Domain

2011 ◽  
Vol 78 (4) ◽  
Author(s):  
J. Li ◽  
S. S. Law
Keyword(s):  
Degrees Of Freedom ◽  
Sampling Rate ◽  
Impulse Response Function ◽  
Frame Structure ◽  
Frequency Domain Method ◽  
Wavelet Domain ◽  
Data Sampling ◽  
Sampled Data ◽  
Domain Method ◽  
Response Reconstruction

An approach for structural response reconstruction in a substructure using the unit impulse response function in the wavelet domain is presented. The forces at the interface degrees-of-freedom are taken as input excitations on the substructure. The response reconstruction is conducted by transforming the measured responses into responses at other selected locations. The response reconstruction using the proposed wavelet domain method is then compared with that from an existing frequency domain method. Numerical studies on a seven-story plane frame structure are carried out to investigate the accuracy and effectiveness of the proposed method, and the effects of influencing parameters such as the number of sampled data, sampling rate, sensor numbers, and measurement noise are studied.

Online Dynamic Response Reconstruction in the Presence of Observation Outliers

2021 ◽  
Author(s):  
Chaodong Zhang ◽  
Jian’an Li ◽  
Youlin Xu
Keyword(s):  
Kalman Filter ◽  
Dynamic Response ◽  
Numerical Study ◽  
Frame Structure ◽  
System Model ◽  
Measurement Information ◽  
Model Errors ◽  
Process Noise ◽  
Robust Kalman Filter ◽  
Response Reconstruction

Previous studies show that Kalman filter (KF)-based dynamic response reconstruction of a structure has distinct advantages in the aspects of combining the system model with limited measurement information and dealing with system model errors and measurement Gaussian noises. However, because the recursive KF aims to achieve a least-squares estimate of state vector by minimizing a quadratic criterion, observation outliers could dramatically deteriorate the estimator’s performance and considerably reduce the response reconstruction accuracy. This study addresses the KF-based online response reconstruction of a structure in the presence of observation outliers. The outlier-robust Kalman filter (OKF), in which the outlier is discerned and reweighted iteratively to achieve the generalized maximum likelihood (ML) estimate, is used instead of KF for online dynamic response reconstruction. The influences of process noise and outlier duration to response reconstruction are investigated in the numerical study of a simple 5-story frame structure. The experimental work on a simply-supported overhanging steel beam is conducted to testify the effectiveness of the proposed method. The results demonstrate that compared with the KF-based response reconstruction, the proposed OKF-based method is capable of dealing with the observation outliers and producing more accurate response construction in presence of observation outliers.

Leak detection in pipelines by exclusively frequency domain method

2012 ◽  
Vol 55 (3) ◽  
pp. 743-752 ◽  
Author(s):  
XinLei Guo ◽  
KaiLin Yang ◽  
YongXin Guo
Keyword(s):  
Frequency Domain ◽  
Leak Detection ◽  
Frequency Domain Method ◽  
Domain Method
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