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Automated Damage Detection Using Lamb Wave-Based Phase-Sensitive OTDR and Support Vector Machines.

Rizwan Zahoor1, Ester Catalano1, Raffaele Vallifuoco1

  • 1Department of Engineering, Università della Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy.

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Summary

This study introduces a Lamb wave-based damage detection method using automatic classification of signals from a phase-sensitive optical time-domain reflectometer (ϕ-OTDR). The technique accurately identifies and locates damage on metallic plates, demonstrating effective structural health monitoring.

Keywords:
Lamb wavesdistributed optical fiber sensorsstructural health monitoring

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Signal Processing

Background:

  • Structural health monitoring (SHM) is crucial for ensuring the integrity of metallic structures.
  • Lamb waves offer a sensitive method for detecting damage in plates.
  • Existing techniques may lack the spatial resolution for precise damage localization.

Purpose of the Study:

  • To propose and demonstrate an automated damage detection technique for metallic plates.
  • To leverage Lamb wave propagation and advanced sensing for precise damage identification.
  • To validate the method's effectiveness using experimental data.

Main Methods:

  • Excitation of Lamb waves in an aluminum plate using piezoelectric transducers.
  • Acquisition of structural response data with a high-resolution phase-sensitive optical time-domain reflectometer (ϕ-OTDR).
  • Classification of Lamb wave signals using support vector machine (SVM) algorithms trained on experimental data.

Main Results:

  • Accurate detection of a small perturbation (5 g mass) on the aluminum plate.
  • Successful localization of the induced perturbation using the developed technique.
  • Demonstration of the multipoint sensing capability of ϕ-OTDR for damage assessment.

Conclusions:

  • The proposed Lamb wave-based damage detection technique is effective for metallic plates.
  • The integration of ϕ-OTDR and SVM classifiers enables accurate damage detection and localization.
  • This method shows significant potential for advanced structural health monitoring applications.