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Model-assisted validation of a strain-based dense sensor network
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Paper 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.

Paper Details

Date Published: 27 March 2019
PDF: 11 pages
Proc. SPIE 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019, 109702C (27 March 2019); doi: 10.1117/12.2515232
Show Author Affiliations
Jin Yan, Iowa State Univ. of Science and Technology (United States)
Xiaosong Du, Iowa State Univ. of Science and Technology (United States)
Simon Laflamme, Iowa State Univ. of Science and Technology (United States)
Leifur Leifsson, Iowa State Univ. of Science and Technology (United States)
Chao Hu, Iowa State Univ. of Science and Technology (United States)
An Chen, Iowa State Univ. of Science and Technology (United States)


Published in SPIE Proceedings Vol. 10970:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019
Jerome P. Lynch; Haiying Huang; Hoon Sohn; Kon-Well Wang, Editor(s)

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