Issue |
ITM Web Conf.
Volume 44, 2022
International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
|
|
---|---|---|
Article Number | 01005 | |
Number of page(s) | 6 | |
Section | Automation | |
DOI | https://doi.org/10.1051/itmconf/20224401005 | |
Published online | 05 May 2022 |
Coal Mine Safety Monitoring and Alerting System with Smart Helmet
Department of Instrumentation Engineering, Ramrao Adik Institute of Technology, Navi Mumbai, Maharashtra, India
* e-mail: man.rud.rt18@rait.ac.in
** e-mail: shi.sha.rt18@rait.ac.in
*** e-mail: mad.tha.rt18@rait.ac.in
**** e-mail: vivekkaddam@rait.ac.in
Traditional monitoring systems in coal mines are difficult to install, hazardous, and difficult to power. Because of the complexity of the mining environment and the wide range of operations performed in coal mines, it is vital to monitor and maintain the parameters in the background t increase the efficiency and safety of mineworkers. As a result, traditional monitoring methods cannot be relied on to ensure coal workers’ safety. This research represents a ZigBee-based wireless monitoring system using a smart helmet. The presented wireless monitoring system is capable of detecting and transmitting critical parameters in coal mines such as methane gas, high temperature, humidity, and fire. In an emergency, this monitoring system transmits distress signals. A buzzer will sound if emergency conditions are detected, and the monitored variables will be displayed on the user interface machine. Moreover, the Parameters are wirelessly transmitted to the control room, allowing people to determine the safety situation of the mine. This model is easily reprogrammable. Experiments have demonstrated the system’s reliability and stability.
Key words: Coal mine Safety / Monitoring system / IoT / ZigBee / Smart helmet
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.