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Data Acquisition Protocol for Determining Embedded Sensitivity Functions
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Localizing low-velocity impacts on wind turbine blades using multi-sensor ensemble learning with dynamic arbitration.

Botao Ning1, Liang Zeng1, Kaidi Fan1

  • 1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China.

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Summary

This study introduces an advanced impact localization method for composite structures using ensemble learning. The novel framework enhances accuracy by fusing data from multiple sensors, improving structural safety.

Keywords:
Acoustic emissionEnsemble learningLow-velocity impact localizationWind turbine blades

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Area of Science:

  • Materials Science
  • Structural Health Monitoring
  • Artificial Intelligence

Background:

  • Composite structures like wind turbine blades are susceptible to impacts, necessitating effective localization methods for safety.
  • Traditional acoustic emission (AE) methods face challenges with wave propagation models, while deep learning models have performance limitations.
  • Accurate impact localization is vital for maintaining the integrity and operational safety of composite structures throughout their lifecycle.

Purpose of the Study:

  • To develop a high-accuracy, multi-sensor impact localization framework for composite structures.
  • To overcome the limitations of single-model deep learning approaches and traditional AE methods.
  • To propose an ensemble learning strategy for robust and generalized impact detection.

Main Methods:

  • A novel framework employing complete ensemble empirical mode decomposition with adaptive noise and permutation entropy to transform AE signals into 2D images.
  • Independent training of multiple expert models using a multi-scale pyramid attention network on data from different sensors.
  • Decision-level fusion of predictions from multiple models using a dynamic arbitration ensemble strategy (DAES).

Main Results:

  • The proposed multi-sensor decision fusion framework significantly improved impact localization accuracy compared to single-sensor models and data-level fusion.
  • The dynamic arbitration ensemble strategy (DAES) demonstrated high generalization capability, not dependent on specific sensor combinations.
  • The method provides a robust solution for region-level impact localization in complex composite structures.

Conclusions:

  • The developed ensemble learning framework offers a superior solution for impact localization in composite materials.
  • The dynamic arbitration ensemble strategy (DAES) presents a highly generalizable fusion paradigm for structural health monitoring.
  • This approach enhances the safety and reliability of composite structures by enabling precise impact detection.