Predictive maintenance has been “the next big thing” in mining for the better part of a decade. Sensor-equipped equipment, machine learning models predicting failures before they happen, optimised maintenance schedules reducing downtime — the pitch has been consistent and compelling.

The execution has been slower and messier than anyone wanted to admit. But in 2026, several years into production deployments at scale, we finally have enough data to separate what actually works from what remains aspirational.

Where It’s Delivering Results

Haul truck gearbox and differential monitoring. This is the standout success story. Vibration sensors and oil analysis feeding into machine learning models can predict gearbox and differential failures 2-4 weeks in advance with 70-80% accuracy.

Given that an unplanned haul truck failure in the Pilbara can cost $50,000-150,000 in lost production (depending on fleet size and pit logistics), catching these failures early generates real value. Sites running these systems report 15-20% reductions in unplanned haul truck downtime.

Caterpillar’s Cat MineStar and Komatsu’s Komtrax platforms both integrate this capability now, and the third-party retrofit market has grown significantly for sites running mixed fleets.

Conveyor systems. Belt monitoring with inline sensors detecting bearing temperature, belt alignment, and roller condition prevents catastrophic failures. Conveyors are particularly good candidates because they operate continuously in relatively controlled conditions, generating consistent data streams that algorithms can model reliably.

Gold operations with long underground conveyor runs have seen the best results. One site in Western Australia I spoke with reported cutting conveyor-related unplanned stoppages by 40% after implementing comprehensive monitoring.

Fixed plant equipment — crushers, mills, screens. Vibration analysis on crushers and mills catches bearing failures, liner wear issues, and feed problems before they cause shutdowns. The ROI calculation is straightforward: avoiding a single unplanned crusher shutdown that costs 12-24 hours of lost throughput typically pays for the monitoring system for a year.

Where Results Are Mixed

Excavator and loader predictive maintenance. These have been harder nuts to crack. Excavators and loaders operate in highly variable conditions — different materials, different operators, different cycle times throughout the day. The data is noisier, and failure modes are more diverse than haul trucks.

Systems exist and are being used, but prediction accuracy is lower (50-60% range) and false positive rates are higher. This creates a dilemma: do you schedule maintenance based on a prediction that’s wrong 40% of the time? The cost of unnecessary maintenance versus the cost of missed failures is a difficult balance.

Drill rig monitoring. Similar challenges to excavators — highly variable operating conditions, multiple potential failure modes, and data quality issues in dusty environments where sensors get fouled. Some sites are seeing value, but it’s not the universal success story that haul truck monitoring has become.

Why Some Deployments Failed

The unsuccessful deployments I’ve reviewed tend to share common patterns:

Insufficient data quality. Machine learning models need clean, consistent data. Sites that didn’t invest in robust sensor installation, calibration protocols, and data validation pipelines ended up with garbage-in-garbage-out results. A $2 million software platform can’t compensate for sensors that aren’t properly maintained.

Lack of integration with maintenance workflows. The system predicts a failure, but the maintenance planner doesn’t trust it, or there’s no process for acting on the prediction, or the parts aren’t in stock. Predictive maintenance requires changing how maintenance is planned and executed — not just adding software on top of existing reactive processes.

Unrealistic expectations. Some sites expected 90%+ prediction accuracy immediately. Real-world systems take 6-12 months of operation to tune models, eliminate false positives, and build confidence with maintenance teams. Sites that expected instant results often abandoned systems before they had time to mature.

The Economics That Matter

Here’s the calculation that determines whether predictive maintenance makes economic sense:

Cost of monitoring system (sensors, software, integration, ongoing data management): $200K-500K per year depending on fleet size and equipment types.

Value of prevented failures: This is site-specific but typically calculated as avoided production loss plus avoided repair costs. For a large iron ore operation, preventing just 2-3 unplanned haul truck failures per month can justify the entire system cost.

The challenge is that benefits are often hard to quantify precisely. How do you value a failure that didn’t happen? Maintenance teams need to track counterfactual scenarios — what would have happened without the prediction — to prove value. This requires discipline that many sites lack.

What Works in Practice

The sites getting genuine value from predictive maintenance share a few characteristics:

They started narrow. They picked one equipment type, one failure mode, and proved value before expanding. Trying to monitor everything simultaneously is a recipe for complexity and underwhelming results.

They integrated maintenance planners into the deployment team. The people who will use the predictions need to be involved in tuning thresholds, defining alert protocols, and building confidence in the system. This is as much a change management challenge as a technical one.

They invested in data infrastructure. Reliable connectivity from pit to server, proper sensor calibration and maintenance, and data quality monitoring aren’t glamorous, but they’re essential. The sites that skimped on infrastructure universally struggled.

Some operations have worked with specialists in AI strategy support to design their deployments, and the outcomes are noticeably better when predictive maintenance is approached as a business process redesign rather than a technology installation.

Looking Forward

The technology is maturing. What was experimental in 2020-2021 is now production-ready for specific applications. The vendors have learned from early deployments, and the costs have come down as hardware and software mature.

But predictive maintenance isn’t a magic solution. It works best for high-value assets with well-understood failure modes operating in relatively consistent conditions. It’s still developing for equipment in highly variable operating environments.

If you’re evaluating predictive maintenance for your operation, focus on equipment where unplanned failures are both expensive and somewhat predictable. Start small, prove value, and expand gradually. And be prepared to invest in the less exciting parts — data infrastructure, process change, and ongoing tuning — because that’s where success or failure is actually determined.