Predictive Maintenance AI: Are You Ready to Build It?

Predictive maintenance AI is the most requested and least often ready industrial project. Predicting failure requires three things that many operations lack: enough recorded failures of a specific mode, sensors that can actually see the failure developing, and a maintenance process able to act on a warning. Test those three before commissioning anything, because the technology is rarely the constraint.

Readiness test one: do you have failure history?

A model that predicts failure learns from failures. That means recorded events where you can say what failed, when, and ideally why, joined to the sensor data covering the period beforehand. Most operations have maintenance records, but the useful detail is often in free text, the timestamps refer to when the work order was closed rather than when the fault occurred, and the same failure is described three different ways by three engineers.

Take one asset class and count. How many distinct failures of a single mode are in the record, with a defensible date, in a period where sensor data also exists? Dozens is workable. Under ten and you should be honest that you are doing anomaly detection with a prediction label on it, which is still useful but is a different promise. Cleaning up how failures are recorded from today onwards is often the highest-value thing a business can do in its first six months.

Readiness test two: can the sensors see it?

Failures announce themselves physically, and only some of those announcements reach your historian. A bearing degrading shows in vibration long before it shows in temperature, and if you sample temperature every fifteen minutes you will see the failure at roughly the same time as the operator does. For each failure mode you care about, ask which physical signal changes first and whether you record it at a rate fast enough to detect the change.

This is where the industrial reality bites. Much existing instrumentation was specified for control and compliance rather than diagnosis, so it captures the right variables at the wrong resolution. The good news is that the audit tells you exactly which additional measurement to add, which is a far cheaper conversation than a general instrumentation upgrade. We go further into the data groundwork in our piece on starting industrial AI with the data you already have.

Readiness test three: could you act on the warning?

The most overlooked test. If the system says a pump will likely fail within three weeks, what changes? If spares have a twelve-week lead time, if the plant cannot be taken offline outside a scheduled shutdown, or if the maintenance team is already at capacity, then a perfect prediction produces anxiety rather than value. Predictive maintenance pays when the warning window is longer than your response time and the response has somewhere to go in the schedule.

Work out the value per avoided failure before building: lost production, secondary damage, expedited parts, overtime. That number sets the sensible budget, and it occasionally reveals that a spare on the shelf is a better investment than a model.

If you are not ready yet

Do the useful interim work rather than nothing. Standardise failure coding so next year's data is labelled. Add the one measurement the audit identified. Deploy anomaly detection on what you have, since it needs no failure labels and starts generating properly recorded events. Then revisit prediction with a year of clean history behind you.

When the three tests pass, the build is an AI Service Build: the prediction delivered as a maintainable capability inside your existing maintenance systems, with retraining and drift monitoring designed in, so it keeps working when the plant, the sensors and the failure patterns move on. The sector context sits on our industrial operations page.