Predictive maintenance analytics
AI reads your equipment data to anticipate failures and alert before a breakdown, so maintenance is planned at the right time.
- Domain
- AI for productivity
- Complexity
- High
- Typical timeline
- 6 to 12 weeks
Predictive maintenance analytics uses AI over equipment data to anticipate failures and warn before downtime, so production keeps running and work is planned at the right time.
What it is
Models that read the signals machines produce (sensors, counters, running logs) and learn what normal behaviour looks like. When patterns start drifting from the usual, the system flags the risk and alerts the team early, instead of waiting for the failure. Maintenance stops being only a reaction to a breakdown or a fixed calendar, and starts happening when the data says it is needed.
Where it pays off
It makes sense in industrial SMEs where an unplanned stoppage is costly: in lost production, in emergency callouts, and in repairs bigger than they would be if caught early. It helps anticipate problems, plan interventions for the least disruptive moments, and extend the working life of equipment. The more critical the asset, the more it pays to see trouble coming.
How we build it
We start with the data the equipment already produces and the history of failures and interventions. We prepare and validate the model against that data, agree with your team which alerts are useful (and the threshold at which they should fire) and integrate with the maintenance systems in use. Then we monitor, measure false alarms, and refine over time.
Frequently asked questions
Do I need new sensors?
Often not. Most equipment already records more than gets used, and the first step is to see what the current systems already say. New sensors only when the reading that matters is genuinely missing.
Does it warn before the breakdown happens?
It warns when behaviour leaves what is normal for that equipment, which is usually before anyone notices. It is not a crystal ball: it is the difference between reacting to the stoppage and reacting to the drift.
And false alarms?
They are this workflow's real risk, because a system that cries wolf stops being read. We tune the thresholds against your operation's own history and start deliberately conservative: fewer alerts, more confidence in each one.
Investment in AI and automation may be eligible for public support in Portugal, subject to the calls in force. We don't handle applications and we're not a funding intermediary.