JMA_Journal@JMA_Journal

JMA is a monthly publication supervised by China Coal Technology & Engineering Group (CCTEG) and sponsored by CCTEG Changzhou Research Institute.

 Jiangsu Province, China
gkzdh.cn/
Joined December 2024
453 Photos and videos
Sensorless control for permanent-magnet hoists achieves ±0.2 r/min speed error and <0.01 rad rotor-position error under time-varying loads. #Mining #Hoisting #SmartMining scienceopen.com/document?vid…
Multi-source signal fusion + BA-SMO boosts mine belt conveyor fault diagnosis to 97.8% accuracy, with 32.6 ms recognition time and <5% missed detection. #Mining #AI #SmartMining scienceopen.com/document?vid…
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Autonomous open-pit haulage needs more than self-driving: new requirements cover perception, navigation, obstacle avoidance, V2X, safety, remote control, and operation in harsh mining environments. #Mining #AutonomousDriving #SmartMining scienceopen.com/document?vid…

Research and development of unmanned transportation standards for open-pit mines

The construction of autonomous transportation systems in open-pit mines is an effective measure to reduce on-site personnel, avoid or reduce accidents, and improve production efficiency and equipment...

scienceopen.com
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Semantic landmarks + multi-sensor SLAM enable autonomous underground navigation, with 0.038 m error per 100 m across a 2.4 km mine roadway and 3.3 s average relocalization. #Mining #Robotics #SmartMining scienceopen.com/document?vid…
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MG-AAE enables label-free idler fault detection using only normal sounds, achieving 95.47% AUC on real mine data and 84.2% AUC under strong noise. #CoalMining #AI #SmartMining scienceopen.com/document?vid…
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DAS enables continuous monitoring of gangue slurry pipelines, with intelligent blockage identification reaching up to 92.72% accuracy and 92.39% F1. #Mining #SmartMining #AI scienceopen.com/document?vid…
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Smarter mine-face speech separation improves SI-SNRi by up to 3.52 dB while using fewer parameters, enabling clearer underground communication and low-power deployment on NPU-equipped edge devices. #Mining #AI #SmartMining
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Optimized VMD + Random Forest improves fault line selection in mine power systems, achieving 98.3% simulation and 97.5% experimental accuracy under varied fault conditions. #Mining #MineSafety scienceopen.com/document?vid…
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Mine hoisting health monitoring is advancing through fixed and mobile sensing, AI-based assessment, and digital twins—enabling smarter monitoring of drums, shafts, ropes, and bearings under harsh conditions. #Mining #AI #SmartMining scienceopen.com/document?vid…
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Smarter belt conveyor health monitoring is evolving from single-signal methods to multi-source fusion, adaptive preprocessing, and deep learning—helping tackle harsh environments, scarce fault samples, and weak generalization. #CoalMining #AI #SmartMining scienceopen.com/document?vid…
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MTSFCL boosts mining bearing fault diagnosis with multimodal fusion, reaching 99.94% accuracy and 99.28% across conditions. Even with only 5 samples/class, accuracy reached 94.12%. #Mining #AI #FaultDiagnosis scienceopen.com/document?vid…
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Edge-side belt conveyor fault diagnosis achieves 98.54% accuracy. Lightweighting cuts parameters, computation, model size, and memory use by 22.7–24.5%, with <1% performance loss. #CoalMining #AI #SmartMining scienceopen.com/document?vid…
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Smarter tracked-robot obstacle avoid combines visible + infrared sensing with deep reinforcement learning, achieving ≤0.4 m position error & no collisions in challenging coal mine roadways.#Mining #Robotics scienceopen.com/document?vid…
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A quadruped inspection robot tackles thin-seam mine challenges, reaching 15.8 m/min, walking stably on 10° slopes, and crossing 100 mm obstacles. #CoalMining #Robotics #SmartMining scienceopen.com/document?vid…
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Active-passive dual-source CT improves rock burst hazard detection in deep coal pillars, with over 80% of microseismic sources located in identified high-velocity zones. #CoalMining #RockBurst #MineSafety scienceopen.com/document?vid…
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Lightweight coal-flow detection with improved DeepLabv3+: 85.58% fewer parameters and 113 FPS, enabling real-time edge deployment for intelligent conveyor speed control. #CoalMining #AI #SmartMining scienceopen.com/document?vid…
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Smarter coal gangue volume measurement tackles occlusion and point-cloud loss during conveying. The method achieved 8.92% average error and a 95.89% qualification rate. #CoalMining #AI #ComputerVisio scienceopen.com/document?vid…
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An integrated intelligent mine ventilation system enables real-time monitoring, anomaly diagnosis, and optimal airflow control, with air-volume calculation error ≤8.38% and wind-resistance error of 2.11%. #MineVentilation #AI #SmartMining scienceopen.com/document?vid…
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Borehole electromagnetic radiation monitoring enables deep stress mapping in ultra-thick coal seams, identifying stress zones and tracking pressure-relief evolution. #CoalMining #MineSafety #Monitoring scienceopen.com/document?vid…
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Goaf groups can significantly weaken open-pit slope stability. Considering the “three zones” effect reduced stability coefficients from 2.038→1.637 and 2.634→1.685. #Mining #SlopeStability #Geotechnical scienceopen.com/document?vid…
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