What is predictive maintenance and how does AI enable it?

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Predictive maintenance (PdM) is a condition-based maintenance strategy that uses machine learning models to predict equipment failures before they occur. Unlike time-based preventive maintenance (which schedules maintenance at fixed intervals regardless of actual equipment condition) or reactive maintenance (which waits for failure before acting), predictive maintenance intervenes at the optimal moment — when the data indicates failure is imminent but before it has occurred.

**How Predictive Maintenance Works**

Sensors on machinery collect real-time data — vibration, temperature, pressure, electrical current, acoustic emissions, oil viscosity — which is fed into AI models trained to recognise the signatures of impending failure. Each type of failure has a characteristic data signature: a bearing failure produces a specific vibration frequency pattern weeks before it causes a breakdown; a motor overheating event is preceded by a gradual temperature rise that deviates from the normal operating envelope; a hydraulic seal failure is preceded by pressure fluctuation patterns that trained models can identify.

The system generates a remaining useful life (RUL) estimate for each monitored asset, triggering maintenance work orders when the probability of failure exceeds a defined threshold — typically 70–80% within the next 14 days. This gives maintenance teams a planning window to schedule the repair during a planned downtime window rather than responding to an emergency breakdown.

**Industry Applications in Australia**

In Australian mining, PresciaIQ's predictive maintenance models analyse sensor data from haul trucks, conveyors, and processing equipment to forecast failure probability weeks ahead, reducing downtime costs by up to 60%. In manufacturing, predictive maintenance integrates with SCADA and MES systems to monitor production line equipment, reducing unplanned downtime by 30–50% and extending asset life by 20–40%. In logistics, fleet predictive maintenance predicts vehicle breakdowns before they strand drivers or delay deliveries, reducing roadside breakdown incidents by 40–60%.

**The Cost Case for Predictive Maintenance**

The financial case for predictive maintenance is compelling. A single unplanned shutdown of a production line in Australian manufacturing typically costs $50,000–$500,000 in lost production, emergency labour, and expedited parts. A predictive maintenance system that prevents even two unplanned shutdowns per year delivers ROI that dwarfs the implementation cost. Compared to time-based preventive maintenance, predictive maintenance also reduces unnecessary maintenance interventions by 20–30% — because maintenance is only performed when the data indicates it is needed, not on an arbitrary schedule.

**Implementation Requirements**

Predictive maintenance requires sensor data from the equipment being monitored. Most modern industrial equipment already has built-in sensors; older equipment can be retrofitted with IoT sensor packages at a cost of $500–$5,000 per asset. PresciaIQ's implementation process includes: sensor connectivity assessment and IoT integration (weeks 1–2), data pipeline setup and historical data collection (weeks 2–4), model training and validation (weeks 4–8), and deployment with maintenance team training (weeks 8–12). Total implementation cost for a 10–50 asset deployment typically ranges from $25,000–$80,000.

What is predictive maintenance and how does AI enable it?

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