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Smart Sensors for Real-Time Mining Hazard Detection
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Smart Sensors for Real-Time Mining Hazard Detection

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 10 June 2026 | Viewed by 314

Special Issue Editor

Dr. Chenghao Wang

E-Mail Website
Guest Editor
Mining Science Center, China University of Mining and Technology, Nanhu Campus, Xuzhou 221116, China
Interests: mining hazard detection; real-time monitoring; disaster early warning; gas early warning; sensing materials; multi-parameter fusion; harsh environment adaptation; safe mining production

Special Issue Information

Dear Colleagues,

Mining operations have long been threatened by sudden hazards like gas outbursts and roof collapses. Traditional detection technologies, with delayed responses and single-dimensional monitoring, can hardly meet real-time prevention and control needs. Smart sensors, leveraging advantages of high sensitivity, real-time data interaction, and multi-parameter collaborative analysis, have become key to solving this problem. Focusing on "Smart Sensors for Real-Time Mining Hazard Detection", this Special Issue invites scholars to share achievements in sensing material innovation, algorithm optimization, and harsh environment adaptation to promote technology transformation.

This theme aligns well with Sensors: it falls under the journal’s key focus on "sensing technology innovation in specific scenarios", and its research on multi-parameter fusion and engineering applications responds directly to the journal’s coverage of "sensing system integration and cross-field applications", providing readers with cutting-edge references in the mining field.

Dr. Chenghao Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart sensors
  • mining hazard detection
  • real-time monitoring
  • disaster early warning
  • gas early warning
  • sensing materials
  • multi-parameter fusion
  • harsh environment adaptation
  • safe mining production

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Published Papers (1 paper)

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Research

27 pages, 3368 KB  
Article
Abnormal Pressure Event Recognition and Dynamic Prediction Method for Fully Mechanized Mining Working Face Based on GRU-AM
by Kai Qin, Longyong Shu, Zhidang Chen, Yan Zhao and Yunpeng Li
Sensors 2025, 25(23), 7336; https://doi.org/10.3390/s25237336 - 2 Dec 2025
Viewed by 222
Abstract
Accurate identification and prediction of abnormal strata pressure in intelligent longwall mining faces are essential for ensuring mine safety and production efficiency. Although machine learning has been increasingly applied to hydraulic support resistance prediction, challenges remain in capturing the strong temporal dependency and [...] Read more.
Accurate identification and prediction of abnormal strata pressure in intelligent longwall mining faces are essential for ensuring mine safety and production efficiency. Although machine learning has been increasingly applied to hydraulic support resistance prediction, challenges remain in capturing the strong temporal dependency and periodic pressure characteristics associated with strata behavior. In this study, a novel abnormal strata pressure identification and prediction framework based on the Gated Recurrent Unit (GRU) integrated with an attention mechanism (AM) is proposed for fully mechanized coal mining faces. The model is designed to capture both short-term fluctuations and long-term cyclic characteristics of support resistance, thereby enhancing its sensitivity to dynamic loading conditions and precursory abnormal pressure signals. Results indicate that the proposed GRU-AM model achieves high prediction accuracy for both single-support and multi-support scenarios, with the predicted resistance closely matching the measured values. Compared with conventional LSTM and CNN models, GRU-AM demonstrates consistently improved performance across multiple evaluation metrics, including RMSE, MAE, MAPE, and Pearson correlation coefficient (R), in both short-step and long-step prediction tasks. At a 1 min step length, the model achieves an overall Accuracy of 0.9741 for abnormal pressure identification, and maintains a high Accuracy of 0.9195 at a 10 min step length. Field application across different mining conditions further confirms the robustness, computational efficiency, and practical reliability of the proposed method. These results demonstrate that the GRU-AM framework provides an effective and scalable solution for real-time abnormal strata pressure recognition and early warning in intelligent coal mining environments. Full article
(This article belongs to the Special Issue Smart Sensors for Real-Time Mining Hazard Detection)
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