PREDICTIVE MAINTENANCE SIGNAL CAPTURE STARTER PACK (120 DAYS)
ACQUIRES VIBRATION, TEMPERATURE, AND ELECTRICAL SIGNALS FROM MACHINES TO ENABLE EARLY FAILURE DETECTION AND CONDITION MONITORING.
Start with one dataset. Prove value. Scale only when it makes sense. Yes, that’s allowed.
Common symptoms
PdM discussions start without signal history.
Maintenance depends on experience.
Vendors offer AI without data foundation.
Improvements wait for shutdowns.
solution
OUR APPROACH
(simple and a bit refreshing)
We start with signal capture, not prediction.
We connect it using:
- FIWARE / NGSI-LD Smart Data Models.
- The DCaaS data layer (already part of FiWAREBox).
- Your existing systems and IT infrastructure.
No disruption.
No platform migration.
No “strategic masterplan alignment committees”.
No platform migration.
No “strategic masterplan alignment committees”.
Once the first dataset is flowing →
transparency, dashboards, and real decisions become possible.
Then we expand - only if it’s worth it.
What You Get (IN 120 DAYS)
- Signal datasets.
- Trend analysis dashboards.
- Early warning thresholds.
This is not a pilot.
This is your operational starting point.
Who This Is For
- Maintenance managers wanting early failure detection.
- Factories with critical rotating or thermal equipment.
- Engineers building their first AI/ML predictive models.
- Integrators preparing to scale condition monitoring across sites.
If that sounds like your FACTORY → we’re aligned already.
Want the architecture walkthrough?
We’ll show:
- How the data actually flows.
- How to start with one dataset.
- How to avoid “Smart Industry over-construction syndrome”.