Design of Coal Mine Monitoring System Based on Internet of Things

Liu, Xiangju; Liu, Lina

doi:10.1007/978-3-662-45049-9_46

Cited by 7 publications

(2 citation statements)

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“…Several studies introduced and discussed a specific modern technique using recent technologies such as AI and its challenges at a particular stage of mining (Ghasemi et al, 2014;Muduli et al, 2018;Ghaychi Afrouz and Westman, 2018;Chakravorty, 2019;Hyder et al, 2019;McGaughey, 2020). Few studies also specifically introduced IoT for a single task in mining such tailing dam monitoring (Sun et al, 2012), coal mine monitoring system (Hu et al, 2013;Bo et al, 2014;Zhou et al, 2017), positioning systems in coal mines (Liu et al, 2010;Liu and Liu, 2014), ventilation and gas monitoring (Qin et al, 2011;Qian et al, 2016;Gillies et al, 2004;Jo and Khan, 2018), roof support (Singh et al, 2018), maintenance and machinery safety (Atkins et al, 2010;Zhang et al, 2014;McNinch et al, 2019). Hyder et al (2019) investigated the application of modern techniques in automation of mining industry; nevertheless, the challenges of applications of IoT are not evaluated in the literature.…”

Section: Cloud Application Layer Visualizationmentioning

confidence: 99%

A Comprehensive Review on Internet of Things (IoT) and its Implications in the Mining Industry

Molaei

Rahimi

Siavoshi

et al. 2020

American Journal of Engineering and Applied Sciences

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The integration of computer-based technologies interacting with industrial machines or home appliances through an interconnected network, for teleoperation, workflow control, switching to autonomous mode, or collecting data automatically using a variety of sensors, is known as Internet of Things (IoT). When applied inside an industrial context, it is possible to immediately benefit from the analytics obtained, contributing to process optimization, machine health, the safety of workers and asset management. IoT can assist real-time platforms in remotely monitoring and operating a complex production system with minimal intervention of humans. Hence it can be beneficial for hazardous industries, such as mining, by increasing the safety of personnel and equipment while reducing operation costs. An ideal smart automated mine could potentially be achievable by gradually taking advantage of IoT. Currently, different sensors are used in mine-related activities, such as geophones in exploration and blast control, piezometers in dewatering and toxic gas detectors in working frontlines. However, a fully integrated automated system is challenging in practice due to infrastructural limitations in communication, data management and storage. Moreover, the tendency of mining companies to continue with traditional methods instead of relying on untested novel techniques decelerates this progress. In this study, the adaptability of the mining industry to IoT systems and its current development is reviewed. Significant challenges of this progress are investigated and recommendations to develop a comprehensive model suited for different mining sections such as exploration, operation and safety considering flexible technologies such as Wireless Sensor Networks and the introduction of Global Data Management.

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Section: Cloud Application Layer Visualizationmentioning

confidence: 99%

A Comprehensive Review on Internet of Things (IoT) and its Implications in the Mining Industry

Molaei

Rahimi

Siavoshi

et al. 2020

American Journal of Engineering and Applied Sciences

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“…Conversely, sensor-based techniques, though adept at realtime surveillance, have been reported to occasionally give rise to false alarms or oversight, particularly in the multifaceted terrains of mines [11,12]. Hence, an emergent need for adopting advanced technological methodologies to augment real-time surveillance of coal mine safety hazards has been highlighted in mine safety literature [13,14].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Safety Hazards in Coal Mines Utilizing an Enhanced YOLOv3 Algorithm

Zheng,

Wang,

Geng

2023

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In coal mining environments, both complexity and potential hazards are inherently present. Responding to the critical need for improved coal mine safety, a method was developed for the real-time surveillance of these hazards using an adapted YOLO algorithm. Initially, an algorithm, which amalgamates attention mechanisms and multi-feature fusion for the detection of safety hazards in coal mines, was presented. Utilizing the YOLOv3 framework, the Gc Net attention module was integrated, a reverse feature fusion pathway was established, and a three-scale prediction module was constructed. Such modifications were designed to identify hazards of various dimensions and configurations, thus augmenting the approach's robustness in intricate situations. Further, the model's loss function underwent optimization to address the imbalances observed in the distribution of positive and negative samples concerning their numbers and complexities. Through experimentation, the efficacy of the proposed method was demonstrated.

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