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.

本项目所用数据集采集自矿山环境下带式输送机托辊的运行状态音频录音，涵盖三种工况：托辊正常运行、托辊壳体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。
该数据库包含两个主要文件夹：<b>dataset（数据集）</b>与<b>code（代码）</b>。
其中<b>dataset</b>文件夹包含三个子文件夹：
<b>wav:</b> 包含19段由19台麦克风录制的WAV格式音频文件，采集自某矿山现场带式输送机托辊的运行音频。其中17段对应托辊正常运行工况，1段对应托辊壳体开裂工况，剩余1段对应托辊完全断裂工况。
<b>csv_dataset:</b> 包含10个子文件夹，每个子文件夹对应以100ms至1000ms不等的帧长从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格式文件，与前述19段音频录音一一对应。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个以200ms为帧长的音频特征数据集。这些数据集整合了17段正常运行工况的音频特征，以及托辊壳体开裂、托辊断裂工况的音频特征，并将整合后的数据集随机打乱100次，以确保各工况特征分布均匀。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>文件夹包含两组代码：
<b>Matlab Code:</b> 该代码可从WAV文件中提取25项音频特征，并以200ms为帧长生成17个音频特征数据集。
<b>Python Code:</b> 该代码可用于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。
本数据集与代码组合可支持带式输送机托辊工况的实时诊断，并为验证音频特征在故障检测中的有效性提供研究基础。

搜集汇总

背景与挑战

背景概述

该数据集包含采矿环境中皮带输送机滚筒的音频记录，涵盖三种操作状态（正常、滚筒壳开裂、滚筒断裂），并提供了用于特征提取和状态监测的代码。数据集支持实时诊断和故障检测，适用于机器学习和深度学习应用。