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Predictive maintenance

Predictive maintenance uses AI to analyze data from sensors attached to equipment, detect warning signs before a failure occurs, and carry out maintenance exactly when it is needed.

About this term
Level
Basic · Technical terms you often meet in AI news
Category
Industry AI
Industries
Manufacturing
Added
2026-10-10
In plain words It is similar to going to the repair shop when engine sounds and dashboard signals suggest "a problem is coming soon," instead of fixing a car after it breaks down completely or replacing parts at every set mileage regardless. The difference is that AI reads, as numbers, even subtle changes that people cannot hear.

How it works

Sensors that measure vibration, temperature, current, pressure and similar signals are attached to equipment such as motors, pumps and bearings, and they collect data continuously. An AI model learns the patterns of normal operation, then looks for anomalies that deviate from those patterns or estimates how much longer a part will last (remaining useful life). When a risk signal appears, it sends an alert to the person in charge and a maintenance schedule is set. The actual maintenance results are recorded and used to improve the model.

Predictive maintenance flowEquipment sensors → Data collection; Data collection → Prediction model; Prediction model → Failure risk alert; Failure risk alert → Maintenance plan; Maintenance plan → Maintenance records; Maintenance records → Prediction model(Retraining)Equipment sensorsVibration ·temperature · currentData collectionPrediction modelAnomalies · remaininguseful lifeFailure risk alertMaintenance planFinal decision by apersonMaintenancerecordsRetraining
Predictive maintenance flow Sensor data passes through the model and leads to alerts and maintenance plans, and maintenance records are fed back to improve the model (dashed line).

Why it matters

When equipment in a factory stops suddenly, the whole production line halts and losses are large. On the other hand, replacing parts on a fixed schedule no matter what means throwing away parts that are still in good condition. Predictive maintenance sits between these two and helps perform maintenance 'only when needed.' This is why it draws attention as a way to reduce both sudden stoppages and unnecessary maintenance.

Three maintenance approaches

Reactive maintenanceFix it when it breaks

  • Risk of sudden stoppage
  • Little to prepare up front

Preventive maintenanceOn a fixed schedule

  • Reduces stoppage risk
  • Replaces parts that are still fine

Predictive maintenanceWhen needed, based on warning signs

  • Reduces both stoppages and waste
  • Requires sensors, data and models

공통 All aim to keep equipment running longer without stoppages

Compares fixing after a failure, replacing on a schedule, and performing maintenance based on warning signs.

Things to know

Failures are rare, so there is often not enough failure data for training. For this reason, teams commonly start with anomaly detection, which catches 'something is different from usual,' rather than failure prediction. If alarms go off too often, people on site start ignoring them, so it is important to reduce false alarms and show maintenance staff the evidence they need to make a judgment. For equipment directly tied to safety, such as power plants or chemical plants, maintenance should not be postponed based on AI judgment alone; legally required inspections and final human confirmation must remain in place.

Example

A factory attaches a vibration sensor to each conveyor motor. When the prediction model detects a vibration pattern that differs from usual, it displays a 'suspected bearing fault' alert on the maintenance team's screen. The maintenance team replaces the bearing during the next planned shutdown, records whether there was actual wear, and uses the result to improve the model.

Related terms

Anomaly detection Time series forecasting Digital twin Smart factory Data drift