AI Strategy · CURA Team · 2026-07-22
Aircraft Predictive Maintenance: You Probably Have a Signal Problem, Not a Model Problem
Most aviation businesses exploring predictive maintenance already have enough data. What they lack is a workflow that gets the right signal to the right planner in time.
Predictive maintenance in aviation has an unusual failure mode. The models are often fine. The OEM feed is often fine. What fails is the last hundred metres: a signal is generated, nobody triages it, and the finding turns up as an unscheduled event anyway.
If you are considering an aircraft predictive maintenance programme, it is worth checking whether your real constraint is prediction or action.
The symptoms of a signal problem
You have condition monitoring or reliability data, and nobody reads all of it. Alerts are reviewed when someone has time. There is no ranking, so a genuine trend and a sensor artefact carry equal weight in the inbox. When something is spotted late, the reason is almost never that the data was absent.
That is a workflow problem wearing a data science costume.
What a triage layer looks like
Filter. Strip the known noise. Sensor artefacts, duplicate alerts, conditions already covered by scheduled work in the plan.
Rank. Score what remains by fleet impact, slot proximity and cost of a late finding. A signal on an aircraft due into your hangar in nine days outranks the same signal on one due in four months, because only one of them is still cheap to act on.
Route. Push the ranked queue to the planner who owns that fleet, inside the tool they already work in. Not another dashboard nobody opens.
Close the loop. Record what the planner did. Over a few months that record tells you which signal classes are worth acting on, which is the data that actually improves the models later.
Where modelling does earn its place
Once triage exists and you can see which signals produce action and which produce nothing, bespoke modelling becomes a sensible next investment, because you finally have labelled outcomes. Doing it in the other order means building models with no reliable measure of whether their output changed a decision.
Practically, where to start
Capture a baseline: how many signals arrive per week, how many are reviewed, how many resulted in planned work, and how many findings were unscheduled. Then build filter, rank and route. Most operators can stand this up within weeks, and the effect on unscheduled events shows in the first quarter.
If you want a view on whether your constraint is prediction or action, book a free consultation and we will look at your current signal flow with you.