Model-Assisted Probability of Detection for Structural Health Monitoring of Flat Plates

Jin, Yan; Laflamme, Simon; Leifsson, Leifur; Tesfahunegn, Yonatan A.; Kozieł, Sławomir

doi:10.1007/978-3-319-93701-4_49

Cited by 3 publications

(1 citation statement)

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“…Work presented in this paper is a continuation of preliminary studies. 19,20 We use the physical surrogate model to compute POD (or Model Assisted POD -MAPOD) based on user-defined detection requirements and algorithms. The application of interest is a strain-based DSN previously developed by the authors.…”

Section: Introductionmentioning

confidence: 99%

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

Jin

Laflamme

Leifsson

et al. 2019

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019

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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.

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Section: Introductionmentioning

confidence: 99%