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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Main Authors: Byung Wan Jo, Rana Muhammad Asad Khan
Format: Article
Language:English
Published: MDPI AG 2017-09-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/7/9/925
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spelling 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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