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Structural Health Monitoring Impact Classification Method Based on Bayesian Neural Network.

Haofan Yu1, Aldyandra Hami Seno1, Zahra Sharif Khodaei1

  • 1Structural Integrity and Health Monitoring Group, Department of Aeronautics, Imperial College London, London SW7 2AZ, UK.

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Summary
This summary is machine-generated.

This study introduces a Bayesian neural network (BNN) for impact classification in structural health monitoring (SHM). The BNN offers reliable impact energy classification and superior computational efficiency compared to multi-ANN, especially for perpendicular impacts.

Keywords:
Bayesian neural networkartificial neural networkimpact classificationpassive sensingstructural health monitoringuncertainty measurement

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Area of Science:

  • Structural Health Monitoring (SHM)
  • Composite Materials
  • Machine Learning for Engineering

Background:

  • Passive sensing for impact characterization often relies on deterministic methods, which struggle with real-world variability in impact conditions.
  • Uncertainty in impact diagnosis arises from variations in location, angle, and energy, necessitating reliability-based approaches.

Purpose of the Study:

  • To propose a novel, reliability-based impact characterization method using a Bayesian neural network (BNN) for multi-class classification and uncertainty quantification.
  • To evaluate the robustness and reliability of the BNN against impact variability, including angled impacts.
  • To compare the BNN's performance, uncertainty quantification, and computational efficiency against a multi-artificial neural network (multi-ANN).

Main Methods:

  • Acquisition of impact data using a piezoelectric (PZT) sensor network on a composite plate.
  • Feature extraction from sensor signals, including transferred energy, frequency at maximum amplitude, and time interval of the largest peak.
  • Development and validation of a BNN model for classifying impact energy levels and quantifying diagnostic uncertainty, with comparative analysis against a multi-ANN.

Main Results:

  • Both BNN and multi-ANN demonstrated high performance (94% and 98% reliable predictions, respectively) for classifying perpendicular impacts.
  • Both models struggled with angled impacts not included in the training data, though uncertainty quantification provided additional diagnostic information.
  • The BNN significantly outperformed the multi-ANN in terms of computational time and resource utilization.

Conclusions:

  • The proposed BNN method provides a reliable approach for impact classification and uncertainty quantification in SHM, outperforming multi-ANN in computational efficiency.
  • While effective for known impact conditions, further research is needed to improve model accuracy for novel scenarios like angled impacts.
  • Uncertainty estimates from the BNN are valuable for interpreting diagnostic confidence and can guide future classification improvements.