Abstract:

Applications of machine learning (ML) to mining and maintenance have been proposed for some time. Results to date have been limited by technical issues like poor quality data, challenges with time series data, sensor reliability, processing power and communications networks. Despite great promise, efforts to apply ML approaches from other industries have also failed to deliver widespread success in part due to lack of mining-specific context and the everchanging geospatial environment in which we operate. Fortunately, our industry is now rapidly evolving. This presentation will discuss how mines that have gone digital are now working with analytics and cloud computing specialists to successfully apply machine learning. We will showcase a handful of recent cases where ML has delivered maintenance insight weeks and months in advance. We will talk about the importance of trust and partnerships to ensure the ML insights are applied correctly. Finally, we will propose a mining future with far more efficient, interactive and data-driven maintenance and planning processes.

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Mr. Devon Wells

Biography