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Particle Swarm Optimization Algorithm for Guided Waves Based Damage Localization Using Fiber Bragg Grating Sensors in

Rohan Soman1, Alex Boyer2, Jee Myung Kim2

  • 1Institute of Fluid Flow Machinery, Polish Academy of Sciences, Fiszera 14, 80-231 Gdansk, Poland.

Sensors (Basel, Switzerland)
|August 26, 2022
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Summary
This summary is machine-generated.

This study demonstrates remotely bonded Fiber Bragg Grating (FBG) sensors for structural health monitoring. A Particle Swarm Optimization (PSO) algorithm enhances damage localization efficiency for real-time applications.

Keywords:
damage localizationfiber Bragg grating (FBG) sensorsguided wavesparticle swarm optimizationremote bonding

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Sensor Technology

Background:

  • Structural health monitoring (SHM) is crucial for reducing maintenance costs and extending structure lifespan.
  • Existing SHM methods ideally require low cost, reliable detection, and accurate localization of minor damage.
  • Fiber Bragg Grating (FBG) sensors offer advantages like light weight, electromagnetic insensitivity, and embeddability.

Purpose of the Study:

  • To provide a proof-of-concept for using remotely bonded FBG sensors for damage localization.
  • To develop a computationally efficient Particle Swarm Optimization (PSO) based algorithm for real-time damage localization.
  • To evaluate the performance of a novel objective function for FBG-based damage detection.

Main Methods:

  • Utilized remotely bonded FBG sensors for guided wave (GW) sensing.
  • Developed and implemented a Particle Swarm Optimization (PSO) algorithm for damage localization.
  • Proposed and tested an objective function based on the exponential elliptical approach.
  • Compared the PSO-based method with brute-force and other existing objective functions.

Main Results:

  • Demonstrated the suitability of the PSO algorithm for accurate damage localization using FBG sensors.
  • The proposed exponential elliptical objective function, tailored for FBG challenges, outperformed brute-force and other methods.
  • The PSO algorithm significantly improved computational efficiency, making it suitable for real-time applications.
  • Successful application of the methodology on an aluminum plate confirmed its effectiveness.

Conclusions:

  • Remotely bonded FBG sensors combined with PSO offer an efficient and accurate solution for structural damage localization.
  • The developed objective function is effective in addressing the specific challenges associated with FBG sensor data.
  • The PSO-based approach enhances computational efficiency, paving the way for real-time structural health monitoring.