Predictive Maintenance

AI models that detect equipment degradation from vibration, temperature and process data — before it becomes unplanned downtime.

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All solutions

VibrationSpectra and trends

TemperatureBearings, windings, discharge

Process contextLoad, speed, pressure

Lead timeWeeks, not hours

// What it is

How Predictive Maintenance works for your plant

Models trained on vibration, temperature and process data learn each machine’s healthy signature and flag degradation as it develops — with enough lead time to plan the intervention, order parts and schedule the work, instead of reacting to a trip.

ASSET HEALTH // K-101

ALERT — WEEKS BEFORE FAILUREWO DRAFTED TO CMMS

// How it works

From data to running system

STEP 01AuditSensor coverage and work-order history reviewed.

STEP 02BaselineHealthy signature learned per asset.

STEP 03DetectDeviations flagged with lead-time estimates.

STEP 04ActAlerts routed to CMMS as planned work.

// Maintenance strategies

Reactive, preventive, predictive

	REACTIVE	PREVENTIVE	PREDICTIVE
Trigger	Failure	Calendar or run-hours	Measured condition and predicted trend
Downtime	Unplanned, longest	Planned, often unnecessary	Planned, only when needed
Spares and labour	Emergency cost	Over-servicing	Right-sized
Data needed	None	Manufacturer intervals	Historian, vibration, CMMS history

// Industries

Where it applies

// Benefits

What it changes

PDM-05 // IMPACT BOARD

Less unplanned downtimeOK Planned, cheaper interventionsOK Longer asset lifeOK Maintenance effort where it mattersOK

FROM THE FIELD

The cheapest intervention is the one you planned three weeks before the trip.

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// FAQ

Common questions

Do we need new vibration sensors?+

Often not. We start from the vibration, temperature and process data you already log; the audit tells you if a critical asset genuinely needs more instrumentation.

What data do we need to start?+

Most engagements start with 6–24 months of historian, lab or document data. We audit coverage in the first week and tell you plainly if it is not enough — before you commit.

How long until we see results?+

A proof of concept on your historical data typically lands in 4–8 weeks. Production deployment follows once the KPI target is met and your team signs off.

Where does it run, and who sees our data?+

On your infrastructure — plant-side servers, your Azure or AWS tenancy, or an air-gapped network. Data never leaves your boundary, and nothing is used to train third-party models.

Who owns the result?+

You do. Handover includes source code, documentation, training sessions and a retraining plan.