Examples of predictive maintenance
Eight concrete cases — pump, motor, gearbox, fan, compressor, heat exchanger, steam trap and switchgear — each traced the same way: the failure mode, the technique that sees it, the signal that appears first, and the decision it should trigger.
| Topic | Key point |
|---|---|
| How to read these examples | Every case below follows the same four steps, because that is the sequence a working programme actually uses: failure mode → technique → first signal → decision . |
| Rotating equipment | Asset Failure mode Technique First signal Decision it triggers Centrifugal pump Rolling-element bearing degradation Vibration analysis, ultrasound ear |
| Static and process equipment | Asset Failure mode Technique First signal Decision it triggers Heat exchanger Fouling Performance monitoring — heat balance from existing instrumentat |
| A case that should not be predictive | The honest example most articles leave out. |
| What makes these examples work in practice | A healthy baseline exists. Every signal above is a change from a reference. |
| Standards behind these cases | The cases above are method, not thresholds. |
How to read these examples
Every case below follows the same four steps, because that is the sequence a working programme actually uses: failure mode → technique → first signal → decision. If you cannot complete all four, the monitoring is not predictive maintenance. It is data collection.
The last step is the one programmes skip. A detected fault that does not change a schedule has cost money and prevented nothing.
Rotating equipment
| Asset | Failure mode | Technique | First signal | Decision it triggers |
|---|---|---|---|---|
| Centrifugal pump | Rolling-element bearing degradation | Vibration analysis, ultrasound earlier still | Bearing defect frequencies appear in the spectrum; friction rises in ultrasound before that | Order the bearing, schedule into the next planned window rather than a breakdown |
| Induction motor | Rotor bar cracking | Motor current signature analysis | Sidebands around line frequency in the current spectrum | Plan a rewind or replacement; check whether starting duty caused it |
| Gearbox | Tooth wear and pitting | Oil and wear-debris analysis, plus vibration | Wear-metal concentration trends upward; gear-mesh sidebands grow | Change the oil, shorten the sampling interval, plan inspection at the next shutdown |
| Fan | Imbalance from deposit build-up or erosion | Vibration analysis | Rising once-per-revolution amplitude | Clean or rebalance before bearing and structural loads accumulate |
| Compressor | Valve wear, internal leakage | Performance monitoring, ultrasound | Specific power drifts up at constant duty | Overhaul decision based on lost energy, not on run hours |
Note what the pump and the motor have in common: the technique is chosen by the failure mode, not by the asset. The same pump needs a different technique for seal failure than for bearing failure.
Static and process equipment
| Asset | Failure mode | Technique | First signal | Decision it triggers |
|---|---|---|---|---|
| Heat exchanger | Fouling | Performance monitoring — heat balance from existing instrumentation | Falling heat-transfer coefficient at comparable duty | Clean on condition instead of on a calendar; see the fouling guide |
| Steam trap | Failed open, blowing live steam | Ultrasound, supported by temperature | Continuous flow signature where cycling is expected | Replace immediately — the loss is continuous and invisible |
| Switchgear and MCC | Loose or corroded connection | Infrared thermography | Temperature rise at a joint relative to the same phase elsewhere | Re-torque or replace at the next safe isolation; severity sets the urgency |
These three are worth separating from rotating equipment because their economics differ. A failed-open steam trap and a hot electrical joint both leak continuously — every hour of delay has a cost, so the detection-to-action time matters more than the detection sensitivity.
A case that should not be predictive
The honest example most articles leave out. A small, redundant, readily-available circulation pump with an installed spare, a low-cost replacement and no safety or quality consequence on failure does not justify monitoring. The correct engineering answer is run-to-failure with a spare on the shelf.
Applying condition monitoring here costs sensor, installation, data and analyst time to avoid a consequence that was already covered by redundancy. Programmes that instrument everything dilute the analyst attention that the genuinely critical assets need.
What makes these examples work in practice
- A healthy baseline exists. Every signal above is a change from a reference. Without a known-good starting point the trend measures nothing.
- Operating context travels with the reading. Speed, load and process state. A compressor's specific power means nothing without the duty it was delivering.
- The interval is shorter than the P-F window. Monthly readings on a fault that develops in three weeks detect failures after they happen.
- Somebody owns the alarm. A named route from finding to work order, with authority to change the schedule.
- Two channels agree before a big call. Instrument drift rarely fakes the same fault in two different physical measurements.
Standards behind these cases
The cases above are method, not thresholds. Where a limit or a severity classification is needed, it belongs to a standard — go to the standard rather than to a blog, including this one.
| Need | Standard to consult |
|---|---|
| Setting up a condition-monitoring programme | ISO 17359 |
| Vibration measurement and severity evaluation | ISO 20816 |
| Diagnostics and data interpretation | ISO 13379 |
| Prognostics | ISO 13381 |
| Analyst qualification by technique | ISO 18436 |
| Failure and maintenance data recording | ISO 14224 |
| Maintenance terminology | EN 13306 |
Frequently asked questions
What is a simple example of predictive maintenance?
Trending vibration on a centrifugal pump. As a rolling-element bearing degrades, characteristic defect frequencies appear and grow in the vibration spectrum. The bearing is ordered and replaced in a planned window, before the failure takes the pump — and often the coupling and seal — with it.
What industries use predictive maintenance most?
Those where unplanned downtime is expensive or hazardous and assets run continuously: power generation, oil and gas, chemicals, pulp and paper, cement, metals, water utilities and large-scale food and beverage. The common factor is continuous operation with costly interruption, not the industry label.
Can predictive maintenance work without new sensors?
Often yes. Performance monitoring uses instrumentation that already exists for process control — flows, temperatures, pressures, motor current. Heat-exchanger fouling and compressor efficiency loss are both detectable from existing measurements before anyone installs a condition-monitoring sensor.
Which assets should not get predictive maintenance?
Cheap, redundant, quickly replaceable assets with no safety, environmental or quality consequence on failure. Run-to-failure with a shelf spare is the correct answer there, and it preserves analyst attention for critical equipment.
How do I know the programme is working?
Measure findings that changed a schedule, not alarms raised. A programme producing many alerts and no rescheduled work is not preventing failures. Recording failures and interventions consistently — ISO 14224 gives a structure — is what lets you tell the difference.
Related guides
Types of predictive maintenance
Seven predictive-maintenance techniques, what physical fault each one actually detects, the assets they suit, the warning time they realistically give, and where each one is blind. Technique is chosen by failure mode, not by asset type.
Predictive vs preventive maintenance
In EN 13306, the European maintenance-terminology standard, predictive maintenance is a sub-type of preventive maintenance, not its opposite. The real dividing line is what triggers the work: a fixed interval, or a measured and forecast condition. Whether prediction is possible at all depends on the asset's P-F interval.
Predictive maintenance: a practical guide
What predictive maintenance is, how it differs from preventive maintenance, which techniques fit which assets, and how to start without boiling the ocean.
Software that helps
Augury
Machine health monitoring for rotating equipment using vibration and AI.
Emerson AMS
Asset management and condition monitoring for process plants.
AVEVA Predictive Analytics
Early-warning analytics for critical process and power assets.