CHALLENGE
From Reactive to Predictive Maintenance
The customer wanted to optimize their maintenance operations by predicting component failures before they occurred. Traditional maintenance strategies often led to unplanned breakdowns, early component replacements, or inefficient scheduling. The key challenge was to identify which components were at risk of failure within a given timeframe (2–12 weeks) and prioritize maintenance accordingly.
They also aimed to answer critical operational questions:
- Which components are predicted to fail before their scheduled replacement?
- Which replacements can be safely postponed?
- In what order should high-value components be replaced to minimize cost and risk?
SOLUTION
Real-Time Predictive Analytics for Haul Trucks
Unikie developed and deployed the customer’s first fully operational predictive maintenance solution for mining operations. The system leverages sensor-based trend data and operational parameters to monitor the health of key truck components, such as transmissions, engines, final drives, and grease pumps.
Using advanced analytics and machine learning models, the solution provides actionable insights to maintenance planners by forecasting failures with high accuracy. For example, certain high-cost components achieved detection rates of over 80% with minimal false alerts. The models were continuously refined in close collaboration between Unikie’s experts and the customer’s technical specialists, ensuring relevance and reliability in real-world conditions.
RESULTS
Reduced Costs, Downtime and Risk
The solution delivered measurable business impact in a demanding operational environment:
Up to 12% reduction in total repair cost by component
Up to 28% reduction in total repair time by component
Up to 83% reduction in unplanned component replacements
Beyond the numbers, the solution enabled safer, better planned maintenance activities, reduced the risk of catastrophic failures, and extended the useful life of several components. Technicians were removed from the “line of fire,” improving occupational safety, and the organization gained a clearer picture of how to prioritize and schedule repairs across their fleet.
