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A Bayesian Approach for Sensor Optimisation in Impact Identification.

Vincenzo Mallardo1, Zahra Sharif Khodaei2, Ferri M H Aliabadi3

  • 1Department of Architecture, University of Ferrara, Via Quartieri 8, 44121 Ferrara, Italy. mlv@unife.it.

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Summary
This summary is machine-generated.

This study optimizes sensor placement for impact identification in composite structures using a Bayesian approach and genetic algorithms. The method enhances structural health monitoring reliability by considering sensor malfunction probabilities.

Keywords:
artificial neural networkgenetic algorithmnon-linear finite element methodprobability of detectionsensor malfunctioningstructural health monitoring

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Area of Science:

  • Engineering
  • Materials Science
  • Computational Mechanics

Background:

  • Structural Health Monitoring (SHM) is crucial for composite structures.
  • Accurate impact identification requires optimal sensor placement.
  • Operational conditions introduce uncertainties and potential sensor malfunctions.

Purpose of the Study:

  • To develop a Bayesian approach for optimizing sensor positions for impact identification in composite structures.
  • To enhance the reliability of SHM systems by incorporating sensor malfunction probabilities.
  • To locate impacts on composite structures under operational loads.

Main Methods:

  • A Bayesian approach combined with a genetic algorithm for sensor optimization.
  • Statistical distributions to represent uncertainty in sensor data.
  • Inclusion of malfunctioning sensor probabilities in the optimization strategy.
  • Development of meta-models for different sensor combinations to assess performance.

Main Results:

  • The proposed optimization strategy effectively identifies optimal sensor placements for impact detection.
  • The Bayesian objective function accurately indicates meta-model performance for impact localization.
  • The inclusion of sensor malfunction probability improves SHM system reliability and robustness.
  • Validation through experimental and numerical examples on composite stiffened panels.

Conclusions:

  • The developed Bayesian optimization framework provides a robust method for sensor placement in composite structures.
  • The approach enhances the reliability of structural health monitoring systems under operational conditions.
  • The algorithm is effective for both uniform and non-uniform impact probability distributions.