How AI can Help Improve Mining Safety and Profitability

Predictive maintenance takes a different approach; it uses powerful AI and machine learning to analyze a broad range of factors and variables in production, and it can determine which variables will lead to certain patterns and behaviors of plant equipment, both mobile and fixed

Fremont, CA: Adoption of digital technology in heavy-duty industries such as mining and mineral processing has been slower than in other industries such as oil and gas. Despite the fact that both are heavy users of large assets, there is a widespread belief that mining is a wear industry in which most equipment deteriorates quickly, and failures are unpredictable. As a result, being able to manage frequent shutdowns, as well as excessive preventive maintenance of critical equipment, has historically been the industry standard practice.

From Becoming Preventive to Predictive

Preventative maintenance entails processes and activities that do not treat or address the underlying problem and, as a result, frequently prolong or delay the inevitable. This frequently results in additional downtime or replacement costs due to early equipment replacement. We've seen numerous examples of additional preventative maintenance activities that are carried out incorrectly, resulting in additional problems. All of this adds to the workload and, as a result, raises the risk of injury.

Predictive maintenance takes a different approach; it uses powerful AI and machine learning to analyze a broad range of factors and variables in production, and it can determine which variables will lead to significant patterns and behaviors of plant equipment, both mobile and fixed. It divides asset conditions into three categories: normal behaviors, anomaly behaviors, and failure patterns. Based on early warning signs and patterns found in operational data, the system can then alert mining companies to failure well in advance of the actual breakdown of the equipment. When the system monitors asset behavior, for instance, if it detects something that is different from normal conditions, it will send an alert to workers – this allows staff to make required adjustments and plan ahead to accommodate downtime if the equipment requires to be serviced, shifting production schedules accordingly.

Predictive maintenance technologies, in addition to predicting potential failures, provide relevant insights to asset operators on variables that must be manipulated to extend the asset's lifecycle. It can recommend specific maintenance actions and spares that may be needed to fix a predicted failure, decreasing the number of hours of man-machine contact on-site and, as a result, improving safety. It also optimizes maintenance scheduling and stages all resources when "wrench turning" is required to reduce the impact on production schedules. Predictive maintenance technology effectively does everything.