Next Article in Journal
System Modeling of a MEMS Vibratory Gyroscope and Integration to Circuit Simulation
Next Article in Special Issue
IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion
Previous Article in Journal
The Design and Characterization of a Prototype Wideband Voltage Sensor Based on a Resistive Divider
Previous Article in Special Issue
Integrated Display and Simulation for Automatic Dependent Surveillance–Broadcast and Traffic Collision Avoidance System Data Fusion
Open AccessArticle

Model-Based Heterogeneous Data Fusion for Reliable Force Estimation in Dynamic Structures under Uncertainties

Civil Engineering and Engineering Mechanics, The University of Arizona, Tucson, AZ 85721, USA
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(11), 2656; https://doi.org/10.3390/s17112656
Received: 31 August 2017 / Revised: 2 November 2017 / Accepted: 6 November 2017 / Published: 17 November 2017
Direct measurements of external forces acting on a structure are infeasible in many cases. The Augmented Kalman Filter (AKF) has several attractive features that can be utilized to solve the inverse problem of identifying applied forces, as it requires the dynamic model and the measured responses of structure at only a few locations. But, the AKF intrinsically suffers from numerical instabilities when accelerations, which are the most common response measurements in structural dynamics, are the only measured responses. Although displacement measurements can be used to overcome the instability issue, the absolute displacement measurements are challenging and expensive for full-scale dynamic structures. In this paper, a reliable model-based data fusion approach to reconstruct dynamic forces applied to structures using heterogeneous structural measurements (i.e., strains and accelerations) in combination with AKF is investigated. The way of incorporating multi-sensor measurements in the AKF is formulated. Then the formulation is implemented and validated through numerical examples considering possible uncertainties in numerical modeling and sensor measurement. A planar truss example was chosen to clearly explain the formulation, while the method and formulation are applicable to other structures as well. View Full-Text
Keywords: force estimation; heterogeneous sensor network; Kalman filtering; multi-metric measurements; structural dynamics force estimation; heterogeneous sensor network; Kalman filtering; multi-metric measurements; structural dynamics
Show Figures

Figure 1

MDPI and ACS Style

Khodabandeloo, B.; Melvin, D.; Jo, H. Model-Based Heterogeneous Data Fusion for Reliable Force Estimation in Dynamic Structures under Uncertainties. Sensors 2017, 17, 2656. https://doi.org/10.3390/s17112656

AMA Style

Khodabandeloo B, Melvin D, Jo H. Model-Based Heterogeneous Data Fusion for Reliable Force Estimation in Dynamic Structures under Uncertainties. Sensors. 2017; 17(11):2656. https://doi.org/10.3390/s17112656

Chicago/Turabian Style

Khodabandeloo, Babak; Melvin, Dyan; Jo, Hongki. 2017. "Model-Based Heterogeneous Data Fusion for Reliable Force Estimation in Dynamic Structures under Uncertainties" Sensors 17, no. 11: 2656. https://doi.org/10.3390/s17112656

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop