Applications of Wireless Sensor Network for Displacement Monitoring in Opencast Coal Mine Slopes
DOI:
https://doi.org/10.18311/jmmf/2023/32032Keywords:
ANSYS Software, Internet of Things, Pit Slope Monitoring, Real-Time Slope Monitoring, Wireless Sensor NetworksAbstract
Coal is the primary source of energy for the production of electricity as well as other industrial purposes. Despite being one of the largest coal producers in the world, India still imports coal to meet domestic demand. Coal is mainly extracted from opencast mines with high mechanization and deeper depths to meet the required coal demand. However, there are many problems with deeper opencast projects, like slope stability and its failure, which can be caused by slope geometry and rock properties. These failures result in equipment damage and human fatalities. It is necessary to monitor the stability of the slope to keep the working conditions safe. Traditional instrument techniques are currently deployed in opencast mines for regular slope monitoring. These traditional instruments usually rely on humans to collect and analyze data offline, which involves huge costs for manpower utilization. Moreover, it only works during daylight, and reliability is also not up to the mark. On the other hand, the latest instruments like Light Detection and Ranging (LiDAR) and Radio Detection and Ranging (RADAR) can efficiently monitor slope movements; those are more expensive and require highly skilled manpower. To eliminate this uncertainty and to obtain real-time data for studying the dynamic behavior of workings, Wireless Sensor Networks (WSN) are introduced. This study uses a node with a sensor and a receiver to collect data based on slope displacement measured in millimeters in a selected opencast mine. Data obtained using a WSN-based monitoring system was observed to be close to the existing total station monitoring method. ANSYS finite element-based numerical modeling software was used for simulating the field conditions and validated the results obtained by the WSN-based sensor's real-time monitoring and existing traditional field monitoring system.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Accepted 2023-10-19
Published 2023-10-19
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