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.