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

Francesco Falcetelli1, Nan Yue2, Leonardo Rossi3

  • 1Department of Industrial Engineering-DIN, University of Bologna, 47121 Forlì, Italy.

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|July 11, 2023
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Summary

A new model-assisted probability of detection (POD) approach for distributed optical fiber sensors (DOFSs) quantifies damage detection performance. This method aids in optimizing SHM systems and understanding sensor capabilities under various conditions.

Keywords:
MAPODdistributed sensingoptical fiber sensorsprobability of detectionstructural health monitoring

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Area of Science:

  • Structural Health Monitoring (SHM)
  • Optical Fiber Sensing Technology
  • Damage Detection Methodologies

Background:

  • Distributed optical fiber sensors (DOFSs) offer efficient sensing for SHM but lack standardized damage detection quantification.
  • Existing probability of detection (POD) methods require extensive testing, limiting practical application.
  • Certification and widespread deployment of DOFSs in SHM are hindered by this methodological gap.

Purpose of the Study:

  • To introduce and validate a model-assisted POD (MAPOD) framework for distributed optical fiber sensors (DOFSs).
  • To assess the influence of various factors on the damage detection performance of DOFSs.
  • To provide a tool for optimizing SHM systems based on DOFSs.

Main Methods:

  • Application of a novel model-assisted POD (MAPOD) framework to DOFSs.
  • Validation using prior experimental data for mode I delamination monitoring in a double-cantilever beam (DCB) specimen.
  • Analysis of quasi-static loading conditions and their impact on sensor performance.

Main Results:

  • The MAPOD framework effectively quantifies DOFS damage detection performance.
  • Factors such as strain transfer, loading conditions, human factors, interrogator resolution, and noise significantly impact detection capabilities.
  • The study demonstrates the sensitivity of DOFS performance to operational and environmental variables.

Conclusions:

  • The MAPOD approach provides a feasible alternative to extensive testing for qualifying DOFS in SHM.
  • This framework enables the study of environmental and operational condition effects on DOFS performance.
  • The MAPOD method supports the design optimization of SHM systems utilizing DOFS.