Evidence map›Paper›PMID 37430727›Full record

ArticleSensors (Basel, Switzerland)2023

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

Francesco Falcetelli, Nan Yue, Leonardo Rossi, Gabriele Bolognini, Filippo Bastianini, Dimitrios Zarouchas, Raffaella Di Sante

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Francesco FalcetelliDepartment of Industrial Engineering-DIN, University of Bologna, 47121 Forlì, Italy.ORCID 0000-0003-2820-776X
Nan YueDepartment of Aerospace Structures and Materials, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands.ORCID 0000-0001-9549-5621
Leonardo RossiIMM Institute, Consiglio Nazionale delle Ricerche, 40129 Bologna, Italy.ORCID 0000-0003-1042-6868
Gabriele BologniniIMM Institute, Consiglio Nazionale delle Ricerche, 40129 Bologna, Italy.
Filippo BastianiniSOCOTEC Photonics, 40069 Zola Predosa, Italy.
Dimitrios ZarouchasCenter of Excellence in Artificial Intelligence for Structures, Prognostics & Health Management, Aerospace Engineering Faculty, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands.
Raffaella Di SanteDepartment of Industrial Engineering-DIN, University of Bologna, 47121 Forlì, Italy.

Funding

European Union 971149
6 · The paper itself

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.

Indexed as

distributed sensingMAPODoptical fiber sensorsprobability of detectionstructural health monitoring

Identifiers

PMID37430727
PMCPMC10221282

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.