La predictive maintenance is a proactive maintenance strategy that uses AI and data analysis For plan the point at which a piece of equipment or a machine is likely to break down. The main objective is to carry out maintenance just before potential failure, maximising equipment life, minimising unplanned downtime and optimising maintenance costs.
In other words, predictive maintenance does not simply repair after breakdowns (corrective maintenance) or to monitor operations scheduled in a fixed maintenance calendar (preventive maintenance). It anticipates problems based on thereal-time data analysis and historical data.
How it works
- Data collection :
- IoT sensors vibration, temperature, pressure, acoustics, electric current, etc.
- Analysis tools thermal cameras, ultrasound, oil (particle detection).
- Predictive analysis :
- Machine Learning Models trained on historical data to detect anomalies.
- Digital twins Virtual replicas simulating the behaviour of equipment in real-life conditions.
- Edge computing local data processing to reduce latency.
- Decision and action :
- Automated alerts integration with CMMS/CMMS systems to plan interventions.
- Conditional maintenance Parts replacement only if necessary.
✔ Benefits
- Cost reduction avoids unplanned downtime (up to -30% in maintenance costs).
- Longer service life equipment (optimising wear and tear).
- Enhanced security Prevention of accidents linked to critical breakdowns.
- Energy efficiency detection of over-consumption (e.g. misaligned motors).
Sector-specific applications
| Sector | Example of use | Technology used |
|---|---|---|
| Aerospace | Monitoring aircraft turbines. | Vibration sensors, thermal analysis |
| Energy | Maintenance of wind turbines (detection of cracks in the blades). | Lidar, inspection drones |
| Automotive | Real-time monitoring of assembly line robots. | IoT, spectral analysis |
| Health | Monitoring medical equipment (MRI, scanners). | Anomaly detection algorithms |
⚠ Challenges to overcome
- Initial investment These include the cost of sensors, cloud platforms and data science skills.
- Data quality The need for clean, structured and representative data.
- Cybersecurity risks of hacking into industrial IoT networks (e.g. attacks on SCADA systems).
- Cultural resistance Moving from a curative approach to a data-driven culture.
Stages of implementation
- Instrumentation Installation of sensors on critical equipment.
- Data centralisation Use of IoT platforms (e.g. Azure IoT, AWS IoT Core).
- Model training : creation of algorithms adapted to the business context.
- Operational integration connection to management systems (ERP, CMMS).
Example
Scenario A steelworks uses vibration sensors on its conveyors.
Process :
- The data is analysed by an ML model trained to spot signs of bearing wear.
- An alert is sent 72 hours before a potential failure.
- The maintenance team replaces the bearing during a scheduled shutdown, avoiding 24 hours of lost production.
Future and Innovations
- Generative AI Virtual failure scenarios: creation of virtual failure scenarios to improve predictions.
- Industrial 5G : ultra-fast data transmission for real-time decision-making.
- Autonomous maintenance AI-enabled robot inspectors.
