Open Surface Coal Mine
Predictive maintenance for mobile equipment using AI
Situation
As one of the largest coal producers in the US, the company operates more than 100 haul trucks. Equipment monitoring relies on oil samples, work orders, and telemetry alerts that are largely reviewed manually. Inconsistent oil analysis and alert overload make it difficult to identify degradation early, leading to reactive maintenance and costly unplanned component failures.
Solution
Historical oil samples, work orders, and telemetry data were used to train an AI model to predict component failures up to four months in advance. The model filters noise and surfaces only the most critical alerts in a single dashboard, replacing fragmented reports and third-party tools with one shared source of insight.
Results
The AI model predicts component-level breakdowns months in advance, reducing unplanned maintenance costs by 25%. This improves maintenance planning and inventory optimization, while freeing up time for reliability and maintenance teams to focus on higher-value tasks.
Have a similar challenge?
Let's talk about how we can apply this approach to your operations.
