Audio Datasets of belt conveyor rollers in mines

The dataset for this project comprises audio recordings of the operational states of belt conveyor rollers in a mining environment, covering three conditions: normal roller operation, roller shell cracking, and roller breakage. Combined with machine learning models, this dataset can be used for real-time diagnosis of roller operational states. The database contains two main folders: <b>dataset</b> and <b>code</b>.The dataset folder includes three subfolders:<b>wav: </b>Contains 19 WAV files recorded from 19 microphones, capturing the audio data of belt conveyor rollers in a mining site. Of these, 17 files represent normal roller operation, 1 file captures the audio of a roller with shell cracking, and 1 file captures the audio of a roller with complete breakage.<b>csv_dataset: </b>Contains 10 subfolders, each representing audio feature datasets extracted from the WAV files with frame lengths ranging from 100ms to 1000ms. Each subfolder contains 19 CSV files, corresponding to the 19 audio recordings. The feature datasets within different frame-length subfolders should not be used interchangeably.<b>test_dataset:</b> Contains 17 audio feature datasets with a 200ms frame length. These datasets include features from 17 normal operation recordings combined with features from the roller shell cracking and roller breakage recordings. The combined datasets are shuffled 100 times to ensure even distribution of features from each operational state. This dataset was used for validating the accuracy and usability of the audio feature datasets for real-time monitoring of roller states in the paper.The code folder contains two sets of code:<b>Matlab Code: </b>This code extracts 25 audio features from the WAV files and generates the 17 audio feature datasets using a 200ms frame length.<b>Python Code: </b>This code validates the accuracy and usability of the audio feature datasets in real-time monitoring of belt conveyor roller operational states.This dataset and code combination supports the real-time diagnosis of belt conveyor roller conditions and provides a foundation for validating the effectiveness of audio features in fault detection.本项目所用数据集包含采矿环境下带式输送机托辊（belt conveyor rollers）的运行状态音频录制样本，涵盖三类工况：托辊正常运行、托辊外壳开裂以及托辊完全断裂。结合机器学习模型，该数据集可用于托辊运行状态的实时诊断。该数据库包含两个核心文件夹：<b>dataset</b>与<b>code</b>。其中<b>dataset</b>文件夹下设三个子文件夹：<b>wav</b>：包含19个WAV格式音频文件，由19个麦克风录制，采集自采矿现场的带式输送机托辊音频数据。其中17个文件对应托辊正常运行工况，1个文件为托辊外壳开裂工况的音频样本，剩余1个文件为托辊完全断裂工况的音频样本。<b>csv_dataset</b>：包含10个子文件夹，每个子文件夹对应以100ms至1000ms间某一固定帧长从WAV文件中提取的音频特征数据集。每个子文件夹内含19个CSV文件，与19段音频录制样本一一对应。不同帧长子文件夹内的特征数据集不可混用。<b>test_dataset</b>：包含17组采用200ms帧长的音频特征数据集。该数据集整合了17段正常运行工况的音频特征，以及托辊外壳开裂、完全断裂工况的音频特征，并将整合后的数据集打乱100次以确保各运行状态特征的分布均衡。本数据集用于验证本文中用于托辊状态实时监测的音频特征数据集的准确性与可用性。<b>code</b>文件夹包含两类代码：<b>Matlab Code</b>：该代码可从WAV文件中提取25项音频特征，并以200ms帧长生成17组音频特征数据集。<b>Python Code</b>：该代码可验证音频特征数据集在带式输送机托辊运行状态实时监测中的准确性与可用性。本数据集与代码的组合方案，可为带式输送机托辊运行状态的实时诊断提供支撑，并为验证音频特征在故障检测中的有效性提供研究基础。