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Related Experiment Video

Updated: Sep 26, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

FDNet: Knowledge and Data Fusion-Driven Deep Neural Network for Coal Burst Prediction.

Anye Cao^1,2,3, Yaoqi Liu¹, Xu Yang⁴

¹School of Mines, China University of Mining and Technology, Xuzhou 221116, China.

Sensors (Basel, Switzerland)

|April 23, 2022

Summary

This summary is machine-generated.

This study introduces FDNet, a novel deep neural network for predicting coal bursts. FDNet enhances safety by fusing expert knowledge and seismic data, improving prediction accuracy.

Keywords:

coal burst coal mine safety deep neural network fusion-driven

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A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System

Published on: June 12, 2019

Area of Science:

Mining Engineering
Geophysics
Artificial Intelligence

Background:

Coal burst prediction is critical for mine safety.
Existing methods often struggle with complex seismic data.
Integrating expert knowledge with data-driven approaches is needed.

Purpose of the Study:

To develop FDNet, a deep neural network for accurate coal burst prediction.
To fuse explicit features from physical models with implicit features from seismic data.
To improve the reliability of coal burst prediction in underground mines.

Main Methods:

Developed FDNet, a knowledge and data fusion-driven deep neural network.
Implemented an expert knowledge indicator selection using a subset search strategy.
Utilized deep convolutional neural networks for microseismic data feature extraction.
Employed an attention mechanism for deep fusion of extracted features.

Main Results:

FDNet demonstrated superior performance in engineering experiments at Gaojiapu Coal Mine.
Prediction accuracy improved by 5% compared to state-of-the-art data-driven methods.
Prediction accuracy improved by 16% compared to knowledge-driven methods.

Conclusions:

FDNet effectively integrates expert knowledge and deep learning for coal burst prediction.
The proposed fusion strategy significantly enhances prediction accuracy.
FDNet offers a promising advancement for improving coal mine safety.