Conventional threshold-based methane monitoring systems used are effective yet still have a number of drawbacks, like the need for connectivity, the need of using a great number of sensor modules to monitor wide areas and the nature of the gas leak which might trigger false positive reading in the sensor system.
The ideal option for methane monitoring in mines is the Silabs EFR32xG24 Dev Kit. It has a hardware Machine Learning (ML) accelerator that can be utilized to create unique gas detection systems that employ machine learning models on the edge.
While methane leaks in mining operations are a well-known issue with a variety of commercially available solutions, conventional threshold-based detection systems have some limitations, such as the requirement for remote data processing and the difficulties in ensuring connectivity in mining-specific environments. By integrating all signal acquisition, data processing, and decision-making on a single board that can operate independently from any wireless network and raise an alarm if dangerous trends in the facility's methane levels are detected, edge AI technologies allow users to get around those weak points.
Built for Edge Impulse by the Zalmotek team