How Razor Labs (IMARC examples) improved fleet utilization through predictive maintenance
RUL and failure-risk models guide interventions on trucks and shovels. Fewer catastrophic failures keep fleets moving.
Context
Fleet utilization suffered because unexpected failures frequently removed trucks from service with little warning. These failures disrupted production schedules and increased the cost of maintenance interventions. The mine needed a way to forecast equipment health before breakdowns occurred.
Solution
Machine-learning models were trained on historical failure data and real-time telemetry to estimate remaining useful life and identify components at elevated risk. These models were integrated into dashboards that maintenance teams used to track health trends and plan interventions. The workflow enabled earlier, more informed decision-making.
Results
The approach improved fleet utilization by reducing catastrophic failures and enabling planned rather than reactive maintenance. Operating continuity increased because issues were addressed before they escalated.
Data Inputs
Telemetry, work orders, environmental