Acoustic and deep features extracted from acoustic emission signals for the characterization of biocomposites and glass fiber epoxy composites

Published: 13 September 2022| Version 2 | DOI: 10.17632/zs82gfkkr9.2
Tomaž Kek,
Martin Misson,
Primož Potočnik


INTRODUCTION The dataset contains 12 Excel files with extracted features for the characterization of loaded materials and classification of the source material as biocomposite and glass fiber epoxy composite. Each file contains features for 22323 samples, extracted from acoustic emission measurements on loaded composites. The dataset was collected to examine various configurations of convolutional autoencoders for deep feature extraction, and different machine learning methods for the classification of the source material. INPUTS The inputs of this dataset are composed of two types of extracted features: 1. Standard features 2. Deep features Standard features are extracted from acoustic emission signals as follows: c1: Peak amplitude [nm] – burst signal linear peak amplitude, c2: Burst signal rise-time [µs] – elapsed time after the first threshold crossing and until the burst signal maximum amplitude, c3: Burst signal duration [µs] – elapsed time after the first and until the last threshold crossing of a burst signal, c4: Burst signal energy [au], c5: RMS Background noise [µV], c6: Counts – Number of positive threshold crossings [/], c7: Spectral centroid [Hz], c8: Frequency of the max. amplitude of Fourier transform spectrum [Hz], c9: Frequency of the max. amplitude of continuous wavelet transformation (using the complex Morlet wavelet) [Hz], c10: Partial power of Fourier spectrum between 0 and 75 kHz [/], c11: Partial power of Fourier spectrum between 75 and 150 kHz [/], c12: Partial power of Fourier spectrum between 150 and 300 kHz [/], c13: Partial power of Fourier spectrum between 300 and 475 kHz [/]. Deep features are extracted by using the convolutional autoencoders (CAE) as described in the accompanying paper. Deep features are extracted automatically in an unsupervised manner and therefore don’t have physical meaning but are designed to minimize information loss of the input-output mapping of the CAE. Deep features are denoted as: d1, d2, d3, etc. OUTPUT The outputs in this dataset are collected in the last column of each file and denote the class (labels_bio-0_cfe-1) as: Class = 0: biocomposite Class = 1: fiber epoxy composite FILE NAME DESCRIPTION File names are composed of CAE1 or CA2 prefixes denoting two types of used CAEs, and the remaining part of the file name defines the CAE hyperparameters as “s1-s2-s3-s4-s5-s6-s7”, which stand for: s1: Number of kernels used in 1st convolutional layers, s2: Number of kernels used in 2nd convolutional, and 1st transposed convolutional layers, s3: Number of kernels used in convolutional and transposed convolutional layers of the latent section, s4: Number of neurons in 2nd and 4th FC layer, s5: Number of neurons in the bottleneck (3rd FC) layer. This number also represents the number of extracted deep features per input image. s6: Number of training epochs, s7: The batch size of training samples.



Univerza v Ljubljani


Polymer Matrix Composites, Machine Learning, Feature Extraction, Acoustic Emission, Biocomposites, Convolutional Neural Network, Glass-Fiber Reinforced Plastic, Autoencoder