AI how-to guides Operations and processes
How to do predictive maintenance with AI
Connect sensor readings and maintenance history for your most critical assets, train models that spot the early signs of degradation and turn alerts into work orders in your maintenance system. Technicians validate the alerts and decide the intervention. Start with a few assets where failures are costly and the telemetry is trustworthy.
What AI does
- Reads sensor, SCADA and operating data continuously and learns what normal looks like for each asset.
- Detects anomalies and early signs of degradation before a failure occurs.
- Estimates risk and, where history allows, how much time is left before intervention is needed.
- Recommends the maintenance action and creates the work order in the CMMS when thresholds are crossed.
- Builds a library of failure signatures from confirmed cases.
What stays with people
- Validating alerts on site and deciding whether and when to intervene.
- Safety decisions and any action that affects people or protection systems.
- Balancing maintenance windows against production priorities.
- Recording what was actually found, so the models learn from real outcomes.
How to put it in place
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Rank assets by criticality and data
List lines and assets with their failure modes, the cost of a stop and the sensor data available. Start where failures hurt and the telemetry is trustworthy, not where data is easiest.
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Gather sensor data and maintenance history
Bring together sensor time series, operating context and the work order history with failure codes. Check timestamps, gaps and sensor calibration: models cannot learn from data nobody trusts.
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Start with anomaly detection, then predict
With few recorded failures, begin by detecting deviations from normal behaviour. As confirmed failures accumulate, add models that classify the failure mode and estimate lead time.
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Tune thresholds with maintenance planners
Decide together which alerts trigger an inspection, which create a work order and how much lead time each one needs. Connect the output to the CMMS so alerts arrive where planners already work.
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Run a field pilot and cut false alarms
Let technicians check every alert and record what they find. Use that feedback to remove false positives before extending to more asset classes, or people will stop trusting the alerts.
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Scale across the fleet
Extend asset by asset, align spares, crews and schedules with predicted demand and retrain models as conditions change. Keep safety gates on any automated action.
What you need
- Sensors on the critical assets (vibration, temperature, pressure, current) with reliable data collection.
- Access to SCADA, historians or the IoT platform, within the plant's network security rules.
- Maintenance history in the CMMS with usable failure codes.
- Maintenance planners and technicians available to label and validate alerts.
- Integration with the CMMS to create work orders.
When it is not worth it
- Cheap, non-critical assets where running to failure costs less than monitoring.
- Assets with no sensors and no plan to install them.
- When failures are rare and fixed-interval maintenance is already cheap and effective.
Risks and how to control them
- Too many false alarms: tune thresholds with technicians and measure precision during the pilot.
- Missed failures from too little failure data: combine anomaly detection with expert rules and keep routine inspections on critical assets.
- Model drift after process or equipment changes: monitor performance and retrain when conditions change.
- Exposing industrial networks: keep data flows one-way where possible and follow OT security policies.
- Safety: if AI acts as a safety component of machinery, it may fall under AI Act high-risk rules; keep people in charge of safety decisions and check obligations with your legal team.
How Thinkia does it
We start with asset criticality: lines, sensors and failure modes are ranked by downtime cost and data availability, and we begin where telemetry is trustworthy. Thresholds, lead times and work order integration are then tuned with maintenance planners.
In a field pilot, technicians validate alerts and false positives are reduced before broadening to more asset classes. At fleet scale, spares, crews and schedules align to predicted demand, and continuous learning respects safety gates.
The Thinkia solution behind this guide
- AI predictive maintenanceFix it before it breaks. Not after.
- AI quality assuranceCatch defects before they reach the customer.
Thinkia products involved
Frequently asked questions
What is the difference between preventive and predictive maintenance?
Preventive maintenance follows a fixed calendar or usage interval. Predictive maintenance uses the actual condition of the asset, measured by sensors, to intervene when signs of degradation appear, so parts are not replaced too early or too late.
Do we need a lot of failure data?
Not to start. Anomaly detection learns normal behaviour and flags deviations without many recorded failures. Predicting the specific failure mode and the time left improves as confirmed failures are recorded.
Which sensors are needed?
It depends on the failure modes. Vibration and temperature are common for rotating equipment; pressure, current and oil condition help in other cases. Start from the failure modes that matter and check what is already measured.
Does it replace maintenance technicians?
No. It tells them where to look and when. Technicians validate alerts, carry out the work and record what they find, which is what makes the models better.
Can it work with our existing CMMS?
Yes, as long as the CMMS can be integrated to read history and create work orders. Alerts should arrive in the tool planners already use rather than in a separate dashboard.
Keep exploring
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- Fix the data first or start with AI now: which comes first?
- AI pilot vs production: what actually changes when you scale?