A Model-Assisted Probability of Detection Framework for Optical Fiber Sensors

F. Falcetelli, N. Yue, Leonardo Rossi, Gabriele Bolognini, Filippo Bastianini, D. Zarouchas, Raffaella Di Sante

Research output: Contribution to journalArticleScientificpeer-review

48 Downloads (Pure)

Abstract

Optical fiber sensors (OFSs) represent an efficient sensing solution in various structural health monitoring (SHM) applications. However, a well-defined methodology is still missing to quantify their damage detection performance, preventing their certification and full deployment in SHM. In a recent study, the authors proposed an experimental methodology to qualify distributed OFSs using the concept of probability of detection (POD). Nevertheless, POD curves require considerable testing, which is often not feasible. This study takes a step forward, presenting a model-assisted POD (MAPOD) approach for the first time applied to distributed OFSs (DOFSs). The new MAPOD framework applied to DOFSs is validated through previous experimental results, considering the mode I delamination monitoring of a double-cantilever beam (DCB) specimen under quasi-static loading conditions. The results show how strain transfer, loading conditions, human factors, interrogator resolution, and noise can alter the damage detection capabilities of DOFSs. This MAPOD approach represents a tool to study the effects of varying environmental and operational conditions on SHM systems based on DOFSs and for the design optimization of the monitoring system.
Original languageEnglish
Article number4813
Number of pages24
JournalSensors
Volume23
Issue number10
DOIs
Publication statusPublished - 2023

Fingerprint

Dive into the research topics of 'A Model-Assisted Probability of Detection Framework for Optical Fiber Sensors'. Together they form a unique fingerprint.

Cite this