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Automatic High-Resolution Operational Modal Identification of Thin-Walled Structures Supported by High-Frequency

Tongfa Deng1,2, Yuexin Wang1,2, Jinwen Huang1,2,3

  • 1School of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China.

Materials (Basel, Switzerland)
|October 26, 2024
PubMed
Summary

This study introduces a novel high-resolution frequency domain decomposition (HRFDD) method for structural health monitoring. It combines optical measurements and accelerometers for efficient, accurate modal identification of thin-walled structures.

Keywords:
automated operational modal identificationclustering algorithmoptical dynamic measurementthin-walled structure

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Area of Science:

  • Structural Health Monitoring
  • Optical Measurement Techniques
  • Modal Analysis

Background:

  • High-frequency optical dynamic measurement offers high-resolution spatial information for damage localization in thin-walled structures.
  • Noise and low computational efficiency limit the real-time application of optical methods in structural health monitoring.
  • Existing methods struggle with efficient and automated identification of modal parameters.

Purpose of the Study:

  • To propose a novel high-resolution frequency domain decomposition (HRFDD) method for efficient and accurate modal identification.
  • To integrate optical dynamic measurement with accelerometers and clustering algorithms for enhanced structural health monitoring.
  • To validate the proposed method's effectiveness on a cantilever aluminum plate for real-time applications.

Main Methods:

  • Combining a high-frequency optical system with accelerometers for vibration response measurement.
  • Utilizing the least-squares complex frequency domain (LSCF) method for natural frequency and damping ratio determination.
  • Employing singular value decomposition (SVD) for high-resolution mode shape acquisition and DBSCAN for automated modal parameter identification.

Main Results:

  • Accurate determination of the first nine order modal parameters (natural frequencies, damping ratios, and mode shapes) for a cantilever aluminum plate up to 500 Hz.
  • High-resolution mode shapes were successfully acquired using SVD processing of optical displacement data.
  • Modal parameters were automatically identified using the DBSCAN clustering algorithm, demonstrating high efficiency.

Conclusions:

  • The proposed HRFDD method effectively addresses noise and efficiency limitations in optical dynamic measurements for structural health monitoring.
  • The integrated approach provides accurate and high-resolution modal parameters, closely matching experimental modal analysis and finite element analysis results.
  • This method offers a viable solution for real-time, online structural health monitoring, particularly for obtaining detailed mode shape information.