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Article
Model-assisted validation of a strain-based dense sensor network
Proceedings of SPIE
  • Jin Yan, Iowa State University
  • Xiaosong Du, Iowa State University
  • Simon Laflamme, Iowa State University
  • Leifur Leifsson, Iowa State University
  • Chao Hu, Iowa State University
  • An Chen, Iowa State University
Document Type
Article
Publication Version
Published Version
Publication Date
3-27-2019
DOI
10.1117/12.2515232
Abstract

Recent advances in sensing are empowering the deployment of inexpensive dense sensor networks (DSNs) to conduct structural health monitoring (SHM) on large-scale structural and mechanical systems. There is a need to develop methodologies to facilitate the validation of these DSNs. Such methodologies could yield better designs of DSNs, enabling faster and more accurate monitoring of states for enhancing SHM. This paper investigates a model-assisted approach to validate a DSN of strain gauges under uncertainty. First, an approximate physical representation of the system, termed the physics-driven surrogate, is created based on the sensor network configuration. The representation consists of a state-space model, coupled with an adaptive mechanism based on sliding mode theory, to update the stiffness matrix to best match the measured responses, assuming knowledge of the mass matrix and damping parameters. Second, the physics-driven surrogate model is used to conduct a series of numerical simulations to map damages of interest to relevant features extracted from the synthetic signals that integrate uncertainties propagating through the physical representation. The capacity of the algorithm at detecting and localizing damages is quantified through probability of detection (POD) maps. It follows that such POD maps provide a direct quantification of the DSNs’ capability at conducting its SHM task. The proposed approach is demonstrated using numerical simulations on a cantilevered plate elastically restrained at the root equipped with strain gauges, where the damage of interest is a change in the root’s bending rigidity.

Comments

This article is published as Yan, Jin, Xiaosong Du, Simon Laflamme, Leifur Leifsson, Chao Hu, and An Chen, "Model-assisted validation of a strain-based dense sensor network," Proc. SPIE 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems (2019): 109702C. DOI: 10.1117/12.2515232. Posted with permission.

Copyright Owner
Society of Photo-Optical Instrumentation Engineers (SPIE)
Language
en
File Format
application/pdf
Citation Information
Jin Yan, Xiaosong Du, Simon Laflamme, Leifur Leifsson, et al.. "Model-assisted validation of a strain-based dense sensor network" Proceedings of SPIE Vol. 10970 (2019) p. 109702C
Available at: http://works.bepress.com/leifur-leifsson/18/