Dynamic Response Reconstruction to Supplement the Data Insufficiency

2019 ◽  
pp. 133-146
Author(s):  
Chaodong Zhang ◽  
Jia He ◽  
Xiaohua Zhang
Keyword(s):  
Dynamic Response ◽  
Response Reconstruction

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.

Dynamic response reconstruction for structural health monitoring using densely connected convolutional networks

2020 ◽  
pp. 147592172091688
Author(s):  
Gao Fan ◽  
Jun Li ◽  
Hong Hao
Keyword(s):  
Structural Health Monitoring ◽  
Dynamic Response ◽  
Health Monitoring ◽  
Network Architecture ◽  
Experimental Studies ◽  
Modal Identification ◽  
Structural Health ◽  
Convolutional Networks ◽  
Response Reconstruction ◽  
Training Efficiency

This article proposes a novel dynamic response reconstruction approach for structural health monitoring using densely connected convolutional networks. Skip connection and dense block techniques are carefully applied in the designed network architecture, which greatly facilitates the information flow, and increases the training efficiency and accuracy of feature extraction and propagation with fewer parameters in the network. Sub-pixel shuffling and dropout techniques are used in the designed network and applied to reduce the computational demand and improve training efficiency. The network is trained in a supervised manner, where the input and output are the measurements of the available channels at response available locations and desired channels at response unavailable locations. The proposed densely connected convolutional networks automatically extract the high-level features of the input data and construct the complicated nonlinear relationship between the responses of available and desired locations. Experimental studies are conducted using the measured acceleration responses from Guangzhou New Television Tower to investigate the effects of the locations of available responses, the numbers of available and unavailable channels, and measurement noise. The results demonstrate that the proposed approach can accurately reconstruct the responses in both time and frequency domains with strong noise immunity. The reconstructed response is further used for modal identification to demonstrate the usability and accuracy of the reconstructed responses. The applicability of the proposed approach for structural health monitoring is further proved by the highly consistent modal parameters identified from the reconstructed and true responses.

Numerical and Experimental Study of Clearance Nonlinearities Based on Nonlinear Response Reconstruction

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Jie Liu ◽  
Bing Li ◽  
Huihui Miao ◽  
Anqi He ◽  
Shangkun Zhu
Keyword(s):  
Nonlinear System ◽  
Dynamic Response ◽  
Nonlinear Response ◽  
Reconstruction Method ◽  
Beam Model ◽  
Graph Method ◽  
Displacement Response ◽  
Nonlinear Force ◽  
Response Reconstruction ◽  
Clearance Nonlinearity

With the growing structural complexity and growing demands on structural reliability, nonlinear parameters identification is an efficient approach to provide better understanding of dynamic behaviors of the nonlinear system and contribute significantly to improve system performance. However, the dynamic response at nonlinear location, which cannot always be measured by the sensor, is the basis for most of these identification algorithms, and the clearance nonlinearity, which always exists to degrade the dynamic performance of mechanical structures, is rarely identified in previous studies. In this paper, based on the thought of output feedback which the nonlinear force is viewed as the internal feedback force of the nonlinear system acting on the underlying linear model, a frequency-domain nonlinear response reconstruction method is proposed to reconstruct the dynamic response at the nonlinear location from the arbitrary location where the sensor can be installed. For the clearance nonlinear system, the force graph method which is based on the reconstructed displacement response and nonlinear force is presented to identify the clearance value. The feasibility of the reconstruction method and identification method is verified by simulation data from a cantilever beam model with clearance nonlinearity. A clearance test-bed, which is a continuum structure with adjustable clearance nonlinearity, is designed to verify the effectiveness of proposed methods. The experimental results show that the reconstruction method can precisely reconstruct the displacement response at the clearance location from measured responses at reference locations, and based on the reconstructed response, the force graph method can also precisely identify the clearance parameter.

Sensor placement method for dynamic response reconstruction

2014 ◽  
Vol 333 (9) ◽  
pp. 2469-2482 ◽  
Author(s):  
J. Wang ◽  
S.S. Law ◽  
Q.S. Yang
Keyword(s):  
Dynamic Response ◽  
Sensor Placement ◽  
Placement Method ◽  
Response Reconstruction

Adaptive Mode Selection Integrating Kalman Filter for Dynamic Response Reconstruction

2021 ◽  
Vol 515 ◽  
pp. 116497
Author(s):  
Xiao-Hua Zhang ◽  
Zimo Zhu ◽  
Guo-Kai Yuan ◽  
Songye Zhu
Keyword(s):  
Kalman Filter ◽  
Dynamic Response ◽  
Mode Selection ◽  
Response Reconstruction

Human pilot dynamic response in flight and simulator.

1958 ◽  
Author(s):  
Edward Seckel ◽  
Ian A. M. Hall ◽  
Duane T. McRuer ◽  
David H. Weir
Keyword(s):  
Dynamic Response
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