Predictive Maintenance for Heavy Equipment

AI analyzes vibration, temperature, and acoustic emissions from critical components like haul truck final drives and shovel swing gears to detect subtle anomalies weeks before failure. This enables planned replacement during scheduled downtime, avoiding catastrophic breakdowns that can idle a multi-million dollar asset for days.

• Real Example: A Tier-1 iron ore operator prevented a $2.1M drive system failure on a 400-ton haul truck by acting on an AI alert 23 days in advance.
• ROI Impact: Reduces unplanned downtime by up to 70% for high-criticality assets, protecting production schedules.

Instead of replacing parts on a fixed schedule, AI models predict the true remaining useful life of wear items like hydraulic pumps, filters, and bucket teeth. This shifts maintenance from time-based to condition-based, extending asset life and cutting parts spend.

• Key Benefit: Achieves a 15-30% reduction in direct maintenance material costs by eliminating premature replacements.
• Implementation: Models ingest telemetry (pressure, flow rates, cycle counts) and operational context (load, material hardness) to provide a dynamic health score and replacement forecast.

For mines deploying autonomous haulage systems (AHS), predictive maintenance is non-negotiable. AI uses the rich telemetry from autonomous trucks to build even more accurate failure models, creating a virtuous cycle of reliability.

• Synergy Benefit: Autonomous systems provide consistent, high-frequency operational data, improving model accuracy. Predictive maintenance, in turn, ensures the autonomous fleet achieves its promised availability targets.
• ROI Multiplier: Protects the massive capital investment in autonomy by ensuring the physical assets remain operational. Enables true 'lights-out' haulage operations.