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Machine Learning Meets Compressed Sensing in Vibration-Based Monitoring.

Federica Zonzini1, Antonio Carbone1, Francesca Romano1

  • 1Advanced Research Center on Electronic Systems "Ercole De Castro" (ARCES), University of Bologna, 40136 Bologna, Italy.

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Summary
This summary is machine-generated.

This study introduces a framework using data compression and neural networks for structural health monitoring (SHM), achieving over 96% accuracy in damage detection even with low-cost sensors.

Keywords:
MEMS accelerometersartificial intelligencemodel-assisted takeness-based compressed sensingoperational modal analysisstructural health monitoring

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Area of Science:

  • Engineering
  • Computer Science
  • Data Science

Background:

  • Structural Health Monitoring (SHM) benefits from Artificial Intelligence (AI) but faces challenges in data management and network congestion.
  • Continuous data collection in SHM strains memory resources and necessitates complex communication protocols.

Purpose of the Study:

  • To develop a comprehensive framework for vibration-based structural diagnostics.
  • To address data management challenges in SHM through compression and efficient AI models.
  • To evaluate the impact of low-cost sensors and environmental factors on damage detection accuracy.

Main Methods:

  • Implementation of data compression techniques to reduce data dimensionality.
  • Development of neural network models for binary classification to detect structural damage.
  • Inclusion of environmental factors and simulated MEMS sensor noise in the analysis.

Main Results:

  • Data compression effectively reduced data management overhead.
  • Neural network models achieved high classification scores (accuracy > 96%, precision > 95%) for damage detection.
  • The framework demonstrated robustness against noise from low-cost sensors and environmental variations.

Conclusions:

  • A combined approach of data compression, optimized machine learning, and environmental data enhances SHM.
  • The proposed framework offers an efficient and accurate solution for vibration-based structural diagnostics.
  • The study validates the framework's effectiveness using real-world data from the Z24 bridge case study.