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Reduction in the Sensor Effect on Acoustic Emission Data to Create a Generalizable Library by Data Merging.

Xi Chen1, Nathalie Godin1, Aurélien Doitrand1

  • 1INSA-Lyon, Universite Claude Bernard Lyon 1, CNRS, MATEIS, UMR5510, 69621 Villeurbanne, France.

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This study investigates how sensors impact acoustic emission (AE) signatures. A new method using Principal Component Analysis and Z-score normalization reduces sensor effects, enabling consistent AE data for machine learning databases.

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Area of Science:

  • Materials Science
  • Non-destructive Testing
  • Signal Processing

Background:

  • Acoustic emission (AE) is a powerful non-destructive testing technique.
  • Sensor variability significantly affects AE signal interpretation and data reproducibility.
  • Standardizing AE data acquisition is crucial for reliable analysis and database development.

Purpose of the Study:

  • To analyze the influence of different sensors on acoustic emission signatures.
  • To develop a methodology for mitigating sensor-induced effects in AE measurements.
  • To enable the creation of generalized AE signature libraries for machine learning applications.

Main Methods:

  • Controlled AE experiments using pencil lead breaks on PMMA plates.
  • Comparison of various AE transducers for plate wave reproduction.
  • Application of Principal Component Analysis (PCA) and Z-score normalization for data processing.
  • Utilizing Kruskal-Wallis test for statistical analysis and outlier identification.

Main Results:

  • Different AE sensors exhibit distinct responses, leading to variations in AE descriptors and test results.
  • The proposed methodology effectively reduces sensor effects, yielding a common descriptor set across all sensors.
  • Z-score normalization and outlier identification are key to achieving consistent AE data distributions.

Conclusions:

  • Sensor selection and data processing significantly influence AE signature analysis.
  • The developed procedure standardizes AE data, facilitating the merging of descriptors into a unified library.
  • This work paves the way for generalized AE signature libraries and machine learning-based AE source classification.