Increase production, improve safety, and reduce maintenance costs by edge connected mining operation
The mining industry plays an important role in today’s world economy. However, the industry is currently going through several challenges from falling commodity prices, a skilled labor shortage, and slowing global economy. Such challenges require innovation on many fronts and IoT has the potential to transform the entire mining industry. IoT can help increase production significantly by tracking miners and vehicles and monitoring the status of vehicles, all through the use of real-time data and analytics.
Because mining is a high-risk profession, mining companies are using autonomous trucks and trains as well as tunneling and boring machines to ensure worker safety and increase productivity. These vehicles are equipped with embedded sensors that enable improved safety and route optimization based on real-time vehicle and terrain data. Asset monitoring information can be collected over time to help in predictive maintenance for critical machines and other assets. Mine workers can be connected as well. Health monitoring devices can help evaluate their physical condition and send alerts to proper personnel in case of incidents.
Mining machines generate many terabytes of data each day that can help mining companies optimize their entire operation by improving safety, reducing energy consumption, optimizing processes, and increasing production. However because most mining operations are in very remote locations, this information cannot be uploaded to centralized data centers for analysis and processing due to the sheer volume of data and because reliable networks and cloud connectivity are typically unavailable at these sites. In addition, proactive real-time responses, require extremely low network latency which cloud-only solutions cannot provide.
An edge intelligence solution that runs analytics in close proximity to the mining operation can provide real-time insight and responses without the need for constant connectivity to the cloud. It can also reduce bandwidth and processing costs because only a small subset of the data will need to be transported to the cloud for further analysis.