The use of more advanced analytics eliminates the need for humans to venture deep into the mine in potentially risky conditions to assess issues and figure out what is going on.
FREMONT, CA :- Mining has always been a hazardous profession because extracting minerals, natural gas, petroleum, and other resources necessitate working in dangerous conditions. To extract the resources people still need and are increasingly being forced to travel to harsher climates, such as deep underneath the ocean or deep inside the planet. It is no wonder, then, that mining and resource extraction companies are turning to robotics, autonomous systems, and AI applications of all kinds to reduce risk, optimize profit, and reduce the environmental impact of their operations.
The mining industry employs a large amount of large, expensive machinery to carry out various operations both on-site and at a distance when the products need to be handled. Many of these machines have numerous sensors that provide large amounts of data that provide:
Insight into how the extremely expensive machines are operating.
The conditions in which they operate.
Their performance on specific tasks.
It is important to keep machinery up and running in order for the mining activity to begin. Any downtime or excessive repairs would result in substantial expense and problems for the mining company.
Before the use of machine learning to help provide greater insights into operations, the data coming in from the sensors was simply fed into the control loop, with no attempt to identify patterns or provide predictive analytics value. The application of machine learning has resulted in a change in the way that data is used. The company will gain considerably greater insight into what problems are currently happening, the evolution of how those problems are occurring, and trends that may lead to problems down the road by storing and constantly analyzing the massive quantities of sensor and other operational data.
Furthermore, the use of more advanced analytics eliminates the need for humans to venture deep into the mine in potentially risky conditions to assess issues and figure out what is going on. Predictive analytics also allows for more proactive and effective activities, from repairs to equipment purchases.