Digital clone testing platform for the assessment of SHM systems under uncertainty
File(s)
Accepted version
Author(s)
Giannakeas, Ilias N
Khodaei, Z Sharif
Aliabadi, MH
Type
Journal Article
Abstract
The performance of a Structural Health Monitoring (SHM) system can be assessed using Probability of Detection (PoD) curves, which is a common tool for the evaluation of Non-Destructive Testing (NDT) methods. This study presents a novel digital clone platform to quantify and account for uncertainties that can be detrimental to the reliability of a SHM system. Uncertainties relating to experimental measurement noise and Environmental and Operational Conditions (EOC) are considered during the definition of a threshold value that aims at reliably distinguishing between pristine and damaged signals. At the same time, the variability of impact damage characteristics and uncertainties associated with Lamb waves interaction in composites are captured though the Bayesian calibration of a Finite Element (FE) model using experimental observations. The FE model is integrated within the digital clone testing platform to substitute the experimental testing and generate a statistical sample of distributed impact events at different locations on a composite plate and compute the Model Assisted Probability of Detection (MAPOD). This approach allows the estimation of the system’s performance under different EOC that can be used during the selection and operation of a specific SHM configuration.
Date Issued
2022-01-15
Date Acceptance
2021-06-13
Citation
Mechanical Systems and Signal Processing, 2022, 163, pp.1-20
ISSN
0888-3270
Publisher
Elsevier
Start Page
1
End Page
20
Journal / Book Title
Mechanical Systems and Signal Processing
Volume
163
Copyright Statement
© 2021 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Clean Sky Joint Undertaking
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000697476500003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
671435
Subjects
Science & Technology
Technology
Engineering, Mechanical
Engineering
Bayesian calibration
Damage detection
Finite element modelling of lamb waves
Non-destructive inspection
Model assisted probability of detection
Gaussian process
WAVE-PROPAGATION
DAMAGE
IDENTIFICATION
CALIBRATION
LOCALIZATION
PROBABILITY
SIMULATION
MODEL
Publication Status
Accepted
Article Number
ARTN 108150
Date Publish Online
2021-07-02