Predictive maintenance is an approach to equipment upkeep where the decision to service a machine comes from analysing sensor data (vibration, temperature, power draw) and models that forecast an approaching failure, rather than from a rigid inspection schedule or a reaction to a downtime event that already happened.
Quick Overview
What you’ll learn from this article:
- How predictive maintenance differs from reactive and preventive maintenance
- Which data and sensors form the basis of predictive models in asset upkeep
- A step-by-step plan for rolling out predictive maintenance, from pilot to full deployment
- What conditions need to be met for a rollout to make economic sense
Who this article is for: maintenance managers and production directors, automation engineers planning an IoT sensor rollout, IT managers responsible for integrating production data.
Reading time: 7 minutes
Three maintenance approaches compared
| Approach | When the machine is serviced | Risk |
|---|---|---|
| Reactive maintenance | After a failure occurs | Unplanned downtime, higher repair cost, risk of damage to adjacent components |
| Preventive maintenance | On a fixed schedule (time or number of work cycles) | Servicing a machine that doesn’t yet need it, or missing a failure between inspections |
| Predictive maintenance | Based on data analysis indicating an approaching fault | Requires investment in sensors, historical data and a model — not cost-effective for low-risk machines |
Deloitte’s analyses of predictive maintenance note that its key advantage over preventive maintenance is avoiding two opposing costs: unnecessary servicing of a healthy machine, and a failure between scheduled inspections. Predictive maintenance lets you time servicing closer to a component’s actual wear, instead of relying on an averaged schedule that’s too frequent for some machines and too infrequent for others.
How to roll out predictive maintenance step by step
- Select the highest-risk, highest-cost-of-failure machines for the pilot — not every piece of equipment in a plant needs predictive maintenance; for simple, cheap-to-replace components, reactive maintenance is often a rational choice.
- Install sensors capturing the data relevant to that machine’s failure mode — vibration for bearings, temperature for electric motors, power draw for pumps, depending on what actually precedes a typical fault.
- Collect historical data covering both normal operation and prior failures, so the model has something to learn the warning signs from — without data on actual failures, a model can’t tell normal variance apart from a genuine threat.
- Build or deploy a predictive model and test it in parallel with the existing service schedule, before fully replacing the old process — a parallel period lets you evaluate alarm accuracy without risking the machine.
- Scale the rollout to more machines gradually, prioritising those with a similar risk profile and data availability to the ones from a successful pilot.
The economics of predictive maintenance depend on the machine’s scale and criticality — the cost of sensors, data integration and model upkeep must be lower than the savings from avoided unplanned downtime. The ISO 55001 asset management standard stresses that maintenance strategy decisions should follow from risk analysis and the asset’s lifecycle cost, not from the assumption that newer technology is automatically better — for many simple, cheap components, reactive maintenance remains a rational, cheaper choice than investing in a full predictive system.
Read Also
- IoT Connectivity in the Factory: Network Architecture and Protocols
- Industry 4.0: Digitising the Production Line Step by Step
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Frequently Asked Questions (FAQ)
Does predictive maintenance pay off for every machine in a plant?
No — it depends on the cost of failure versus the cost of deploying sensors and a model for that machine. For cheap, easily replaced components, reactive or preventive maintenance is usually enough; predictive maintenance makes sense for critical machines where downtime carries a high cost.
What data is needed to build a predictive model?
At minimum, sensor data reflecting the machine’s degradation mode (e.g. vibration, temperature) plus a history of actual failures, so the model can learn to recognise the patterns that precede a fault. Without data on actual failures, the model has no reference point for telling normal operation apart from an approaching problem.
How long does a predictive maintenance pilot take?
It depends on historical data availability — if the plant already collects sensor data, a pilot can take a few months. If sensors still need installing and data must be collected over at least one full machine work cycle including a possible failure, the process extends to six to twelve months.
Does predictive maintenance fully replace preventive maintenance?
Not always — in practice, many plants use a mixed approach: predictive maintenance for critical machines with plenty of available data, preventive maintenance for medium-risk machines, and reactive maintenance for the simplest, cheapest components.
What happens when the predictive model produces false alarms?
False alarms are a normal part of early rollout — that’s exactly why the parallel period alongside the old service schedule matters, since it lets you calibrate the model before fully relying on its output. Too many false alarms can lead to alarm fatigue, where the team starts ignoring notifications, which in practice defeats the purpose of the whole rollout.