An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study
Fatal accidents associated with underground coal mines require the implementation of high-level gas monitoring and miner’s localization approaches to promote underground safety and health. This study introduces a real-time monitoring, event-reporting and early-warning platform, based on cluster anal...
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doaj-773e53beee734fd78d894f3162b294fb2020-11-24T20:49:02ZengMDPI AGApplied Sciences2076-34172017-09-017992510.3390/app7090925app7090925An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case StudyByung Wan Jo0Rana Muhammad Asad Khan1Department of Civil and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, KoreaDepartment of Civil and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, KoreaFatal accidents associated with underground coal mines require the implementation of high-level gas monitoring and miner’s localization approaches to promote underground safety and health. This study introduces a real-time monitoring, event-reporting and early-warning platform, based on cluster analysis for outlier detection, spatiotemporal statistical analysis, and an RSS range-based weighted centroid localization algorithm for improving safety management and preventing accidents in underground coal mines. The proposed platform seamlessly integrates monitoring, analyzing, and localization approaches using the Internet of Things (IoT), cloud computing, a real-time operational database, application gateways, and application program interfaces. The prototype has been validated and verified at the operating underground Hassan Kishore coal mine. Sensors for air quality parameters including temperature, humidity, CH4, CO2, and CO demonstrated an excellent performance, with regression constants always greater than 0.97 for each parameter when compared to their commercial equivalent. This framework enables real-time monitoring, identification of abnormal events (>90%), and verification of a miner’s localization (with <1.8 m of error) in the harsh environment of underground mines. The main contribution of this study is the development of an open source, customizable, and cost-effective platform for effectively promoting underground coal mine safety. This system is helpful for solving the problems of accessibility, serviceability, interoperability, and flexibility associated with safety in coal mines.https://www.mdpi.com/2076-3417/7/9/925underground minesevent detectionoutlier detectionInternet of Thingsminer’s localization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Byung Wan Jo Rana Muhammad Asad Khan |
spellingShingle |
Byung Wan Jo Rana Muhammad Asad Khan An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study Applied Sciences underground mines event detection outlier detection Internet of Things miner’s localization |
author_facet |
Byung Wan Jo Rana Muhammad Asad Khan |
author_sort |
Byung Wan Jo |
title |
An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study |
title_short |
An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study |
title_full |
An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study |
title_fullStr |
An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study |
title_full_unstemmed |
An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study |
title_sort |
event reporting and early-warning safety system based on the internet of things for underground coal mines: a case study |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2017-09-01 |
description |
Fatal accidents associated with underground coal mines require the implementation of high-level gas monitoring and miner’s localization approaches to promote underground safety and health. This study introduces a real-time monitoring, event-reporting and early-warning platform, based on cluster analysis for outlier detection, spatiotemporal statistical analysis, and an RSS range-based weighted centroid localization algorithm for improving safety management and preventing accidents in underground coal mines. The proposed platform seamlessly integrates monitoring, analyzing, and localization approaches using the Internet of Things (IoT), cloud computing, a real-time operational database, application gateways, and application program interfaces. The prototype has been validated and verified at the operating underground Hassan Kishore coal mine. Sensors for air quality parameters including temperature, humidity, CH4, CO2, and CO demonstrated an excellent performance, with regression constants always greater than 0.97 for each parameter when compared to their commercial equivalent. This framework enables real-time monitoring, identification of abnormal events (>90%), and verification of a miner’s localization (with <1.8 m of error) in the harsh environment of underground mines. The main contribution of this study is the development of an open source, customizable, and cost-effective platform for effectively promoting underground coal mine safety. This system is helpful for solving the problems of accessibility, serviceability, interoperability, and flexibility associated with safety in coal mines. |
topic |
underground mines event detection outlier detection Internet of Things miner’s localization |
url |
https://www.mdpi.com/2076-3417/7/9/925 |
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