Intelligent Monitoring Systems and Technology for Coal Mine Safety
A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 February 2027 | Viewed by 6
Editor
Interests: machine learning; image processing; computer networks; internet of things; LLM
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Coal mine safety remains a critical challenge due to complex geological conditions and various hazards, including gas, coal dust, water inrush, fire, and roof-fall hazards. Traditional monitoring methods often rely on discrete sensors and manual inspection, which can be insufficient for early warning and rapid response. Recent advances in intelligent monitoring technologies—such as the Internet of Things, advanced sensing, artificial intelligence, big data analytics, digital twins, robotics, and high-speed communication—offer transformative opportunities to enhance underground situational awareness, hazard prediction, and emergency decision-making.
This Special Issue aims to bring together original research and reviews on the development and application of intelligent monitoring systems and technologies for coal mine safety. Topics of interest include, but are not limited to, intelligent sensors and wearable devices; multi-source data fusion; AI-based gas, fire, water, and ground pressure early warning; computer vision for personnel and equipment monitoring; unmanned inspection robots and drones; positioning and communication systems; digital twin and visual analytics platforms; and case studies of intelligent monitoring in underground mines.
We invite contributions that address fundamental theories, key technologies, system integration, and field applications, with the goal of promoting safer, more efficient, and intelligent coal mining operations.
Prof. Dr. Wei Chen
Guest Editor
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Keywords
- coal mine safety
- intelligent monitoring
- internet of things
- artificial intelligence
- multi-source data fusion
- digital twin
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