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Optimal Design of a Sensor Network for Guided Wave-Based Structural Health Monitoring Using Acoustically Coupled

Rohan Soman1, Jee Myung Kim2, Alex Boyer2

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

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
Summary
This summary is machine-generated.

This study optimizes actuator-sensor networks for structural health monitoring (SHM) using Fiber Bragg Grating (FBG) sensors and acoustic coupling. The method significantly reduces instrumentation costs while improving SHM quality.

Keywords:
acoustic couplingfiber Bragg grating (FBG) sensorsguided wavesmulti-objective optimization

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Area of Science:

  • Structural Health Monitoring (SHM)
  • Wave Propagation
  • Sensor Networks

Background:

  • Guided waves (GW) are crucial for large-area structural inspection in SHM.
  • Fiber Bragg Grating (FBG) sensors offer advantages but historically had limited sensitivity for GW sensing.
  • Edge-filtering configurations enhance FBG sensor sensitivity, renewing interest despite high equipment costs.

Purpose of the Study:

  • To develop a cost-effective SHM system by optimizing actuator-sensor networks.
  • To leverage acoustic coupling for interrogating multiple locations with a single FBG sensor.
  • To reduce the number of sensors and actuators required for comprehensive structural monitoring.

Main Methods:

  • A two-step optimization methodology for actuator-sensor networks was developed.
  • The non-sorting genetic algorithm (NSGA-II) was implemented for network optimization.
  • Acoustic coupling was utilized to enable multi-point interrogation with single sensors.

Main Results:

  • The optimization process balanced application demands (coverage) with instrumentation costs.
  • The acoustic coupler network design minimized measurement interference.
  • Experimental validation confirmed the analytical cost function implementation.

Conclusions:

  • The proposed optimization methodology enhances SHM quality.
  • Significant reductions in instrumentation costs are achievable.
  • The approach makes advanced SHM systems more feasible and cost-effective.