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

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.

引言
本数据集包含12个Excel文件，内含用于表征受载材料的提取特征，以及将源材料分类为生物复合材料（biocomposite）和玻璃纤维环氧树脂复合材料（glass fiber epoxy composite）的相关数据。每个文件对应22323个样本的特征，这些特征源自受载复合材料的声发射（acoustic emission）测量结果。本数据集的采集旨在研究用于深度特征提取的各类卷积自编码器（convolutional autoencoder, CAE）配置，以及用于源材料分类的多种机器学习方法。

输入项
本数据集的输入包含两类提取特征：
1. 标准特征
2. 深度特征

标准特征从声发射信号中提取，具体如下：
c1：峰值振幅[nm]——突发信号的线性峰值振幅；
c2：突发信号上升时间[µs]——从首次越过阈值至突发信号达到最大振幅的时长；
c3：突发信号持续时间[µs]——从首次越过阈值至最后一次越过阈值的时长；
c4：突发信号能量[au]；
c5：背景噪声均方根值[µV]；
c6：计数[ / ]——正阈值越过次数；
c7：频谱质心[Hz]；
c8：傅里叶变换频谱最大振幅对应的频率[Hz]；
c9：连续小波变换（采用复Morlet小波（complex Morlet wavelet））最大振幅对应的频率[Hz]；
c10：0~75 kHz区间内傅里叶频谱的部分功率[ / ]；
c11：75~150 kHz区间内傅里叶频谱的部分功率[ / ]；
c12：150~300 kHz区间内傅里叶频谱的部分功率[ / ]；
c13：300~475 kHz区间内傅里叶频谱的部分功率[ / ]。

深度特征通过卷积自编码器（convolutional autoencoder, CAE）提取，具体方法详见配套论文。深度特征以无监督方式自动提取，因此不具备物理意义，其设计目标是最小化卷积自编码器输入-输出映射的信息损失。深度特征记为d1、d2、d3等。

输出项
本数据集的输出项存放在每个文件的最后一列，以类别标签（labels_bio-0_cfe-1）表示，具体如下：
类别=0：生物复合材料（biocomposite）；
类别=1：纤维环氧树脂复合材料（glass fiber epoxy composite）。

文件名说明
文件名以CAE1或CAE2为前缀，代表两种不同的卷积自编码器类型；文件名剩余部分以"s1-s2-s3-s4-s5-s6-s7"的格式定义了卷积自编码器的超参数，各参数含义如下：
s1：第1卷积层使用的卷积核数量；
s2：第2卷积层及第1转置卷积层使用的卷积核数量；
s3：隐空间部分的卷积层与转置卷积层使用的卷积核数量；
s4：第2、第4全连接层的神经元数量；
s5：瓶颈层（第3全连接层）的神经元数量，该数值同时代表每个输入图像提取的深度特征总数；
s6：训练轮次数量；
s7：训练样本的批量大小。