How IoT Can Support Predictive Maintenance

Scheduled maintenance services equipment on a fixed calendar regardless of its actual condition — reliable but often wasteful, since parts get replaced whether they need it or not. Condition-based maintenance uses live sensor readings against a threshold to trigger a check when something looks abnormal, which is a meaningful step up without needing historical modelling.

Predictive maintenance goes further: it tries to estimate how much life a component has left, or how likely it is to fail soon, based on patterns learned from historical data. It's the most valuable stage when it works, and also the one most often oversold, since it depends on data most businesses haven't collected yet.

A predictive model needs three things together: continuous sensor signals, enough historical data to show how those signals behaved in the lead-up to past failures, and — critically — records of when those failures actually happened, so the pattern can be labelled as "this is what a developing fault looked like."

It's this third piece that's usually missing. Plenty of businesses have maintenance logs, but they record what was fixed, not the sensor readings in the days or weeks beforehand, because nobody was capturing that data at the time. Without labelled failure history, a model has nothing to learn the warning pattern from.

Consider a plastics injection moulding unit with recurring bearing failures on two machines. If maintenance records only note "bearing replaced" with a date, and no sensor was running beforehand, there's no failure signature to learn from — the honest first step is to start collecting condition data now and build a genuine predictive case over the following months, not to buy a model today that has nothing to train on.

Feasibility also depends on how consistent the failure mode is. A component that fails the same way each time, with a distinctive lead-up pattern, is a far better predictive candidate than one that fails for many unrelated reasons — some plant equipment is simply not a good fit for prediction yet.

Any model output — a predicted failure window, a confidence score — should be reviewed by someone who understands the equipment before it drives a maintenance action. Sensor data can be misleading in ways a data model alone won't catch: a reading affected by a nearby machine, a sensor mounted slightly off, or a genuine but harmless operating condition that looks abnormal on paper.

This isn't a step to skip in the interest of automation. Treating a model's output as advisory input to an experienced maintenance engineer's decision, rather than as an automatic trigger, is what keeps false predictions from creating unnecessary downtime of their own.

Once a predictive approach is running, the useful measures are how many alerts turned out to be real versus false, how much unplanned downtime was avoided compared with the baseline, and whether component lifecycle cost actually went down once replacements happened closer to true end-of-life rather than on a fixed early schedule.

These numbers should be tracked over a defined period against a baseline recorded before the system started, the same discipline that applies to any monitoring pilot — otherwise it's easy to credit the system for an improvement that had another cause.