Model-based heterogeneous data fusion for reliable force estimation in dynamic structures under uncertainties

Babak Khodabandeloo, Dyan Melvin, Hongki Jo

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number2656
JournalSensors (Switzerland)
Volume17
Issue number11
DOIs
StatePublished - Nov 17 2017

Keywords

  • Force estimation
  • Heterogeneous sensor network
  • Kalman filtering
  • Multi-metric measurements
  • Structural dynamics

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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