Predictive maintenance: what it is and when it pays off
Changing a part that still works costs money. Having it blow up mid-shift costs a lot more. Predictive maintenance lives between those two bills.
Predictive maintenance means touching a machine according to its real wear, measured with sensors. Not according to the calendar. And certainly not after the breakdown.
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The promise is simple to state: change the part just before it fails. Not long before, and not late.
The three strategies
| Strategy | When you act | Typical cost |
|---|---|---|
| Corrective | once it's broken | unplanned stoppage, the most expensive |
| Preventive | every X hours or cycles | parts changed with life left in them |
| Predictive | when the data announces the failure | planned, minimal intervention |
Corrective looks cheap until the night your line stops at three in the morning. Preventive saves you that but throws useful life in the bin: you change by calendar, not by condition. Predictive tries to sit between the two.
Five things worth knowing
1 · It isn't new. Vibration, temperature and oil analysis have been used for decades. What's changed is the cost of the sensor and of the computing, which has dropped far enough to put it within reach of plants that would never have considered it.
2 · Not all your machines deserve it. It pays where the stoppage is expensive, the failure is progressive and it gives warning. A motor degrading will keep warning you. An electronic component that dies outright warns you of nothing.
3 · It needs history. The model learns the pattern that runs ahead of the breakdown. Which means: it needs to have seen breakdowns. With no prior data, your first year is a collection year, and that's that.
4 · It fails in two directions. It warns you of a failure that doesn't come — and you stop the machine for nothing — or it fails to warn you of the one that does. And note, where you set that balance is not a technical decision. It's yours, a business one.
5 · The bottleneck is almost never the model. It's whether the alert reaches somebody who can act, and whether there's a spare. A system that calls out a breakdown two weeks ahead is worth nothing if the part takes six weeks to arrive.
The bridge to 2026
Let me tell you my reservation about this, because I rarely keep it to myself: quite often people start here. And it's usually the hardest project in the whole plant. It wants breakdown history, it wants instrumentation, and it wants time before it proves it's worth anything.
When somebody wants to get going with industrial AI, I nearly always propose the opposite: start with something that produces a result in the same shift. Catch the defect on the piece going past right now. Record traceability without paper. Flag the stoppage the moment it happens, with its reason attached.
Why? Because that gives you the two things predictive maintenance needs and doesn't have on day one: the team's trust and a clean history.
With those two on the table, predictive maintenance stops being a gamble and becomes the obvious next step. Which is where it should have been all along.