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Enhanced Sensor Placement Optimization and Defect Detection in Structural Health Monitoring Using Hybrid PI-DEIM

Minyoung Yun1, Mikhael Tannous1, Chady Ghnatios2

  • 1PIMM Research Laboratory, UMR 8006 CNRS-ENSAM-CNAM, Arts et Metiers Institute of Technology, 151 Boulevard de l'Hôpital, 75013 Paris, France.

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Summary
This summary is machine-generated.

This study presents a new hybrid sensor method for pinpointing critical locations and detecting structural defects. Combining discrete empirical interpolation (DEIM) and permutation importance (PI) yields the most accurate defect detection with minimal error.

Keywords:
discrete empirical interpolation methodmachine learningoptimal sensor placementrandom permutation features importance method

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Area of Science:

  • Structural Health Monitoring
  • Computational Mechanics
  • Data Science

Background:

  • Accurate identification of critical sensor locations is crucial for effective structural health monitoring.
  • Existing methods for sensor placement and defect detection may have limitations in accuracy and speed.
  • Reduced order modeling and optimization algorithms offer potential for enhancing these processes.

Purpose of the Study:

  • To develop a novel hybrid methodology for identifying optimal sensor locations in structural components.
  • To introduce an advanced defect detection approach utilizing these identified sensors.
  • To evaluate the performance of the proposed hybrid method in terms of accuracy and error reduction.

Main Methods:

  • A hybrid approach integrating the discrete empirical interpolation method (DEIM) and permutation importance (PI) was used to determine optimal sensor placements.
  • A semi-intrusive reduced order modeling technique was employed for defect detection.
  • A genetic search algorithm was utilized in conjunction with the reduced order model for fast and reliable defect identification.

Main Results:

  • The proposed hybrid method successfully identified critical sensor locations.
  • Defects were located with low error, particularly when using hybrid sensors that combine critical sensors from both PI and DEIM.
  • The hybrid sensor approach demonstrated superior performance in defect identification compared to using PI or DEIM methods individually.

Conclusions:

  • The developed hybrid methodology offers an effective strategy for optimizing sensor placement in structural components.
  • The combination of DEIM and PI for sensor selection leads to enhanced accuracy in defect detection.
  • The proposed reduced order modeling and genetic search algorithm provide a fast and reliable solution for structural defect identification.