VibraQore
Detect bearing/imbalance faults early and plan interventions to avoid unplanned downtime.
VibraQore is predictive maintenance that fuses vibration, current and thermal signals from the sensors on your motors, pumps, reducers and spindles, forecasts failures up to 14 days out, and turns each forecast into a planned work order in your CMMS.
Who is this for?
Maintenance and reliability managers in plants where an unplanned stop costs more than the part, and plant engineers who have periodic vibration checks and know how much happens between them. It works with the accelerometers, current transformers and thermal cameras you have; a sensor plan for the gaps comes out of the proof of concept.
What problem does it solve?
A periodic vibration route catches the fault that happens to be developing on the day the technician visits. Bearing wear, imbalance, misalignment and looseness build between visits, and the signature that would have given two weeks of warning is in a reading nobody trended.
So the failure arrives as an emergency: overtime, expedited parts, and a production line down at the one hour nobody could afford. The maintenance team is then judged on the breakdown it could not have seen, because the data that would have shown it was never collected continuously.
How does it work?
Sensors stream across the X, Y and Z axes and the system runs FFT analysis continuously, picking out imbalance, misalignment, bearing wear and looseness patterns from 100 Hz to 20 kHz. Models trained on your own equipment's history then forecast failures up to 14 days ahead, with confidence scores and degradation curves rather than a single red light.
Each forecast becomes a work order in SAP PM, IBM Maximo, Fiix or UpKeep, pre-filled with the predicted failure type, the affected asset and the recommended parts. The schedule is prioritised by failure probability, production impact and technician availability, so interventions land in planned downtime and workload balances across shifts.
Accuracy is stated with its conditions. Precision on failure prediction typically runs 75 to 85 percent with a two to four week lead time, varying by asset type and data quality; it is tracked as a core KPI, drift is monitored automatically, and models are reviewed quarterly for seasonal patterns and new baselines.
Capabilities
- Vibration + current + thermal fusion models
- Asset health scoring & remaining-useful-life hints
- Work order suggestions to CMMS
- Parts & labor forecasting
- Drift & seasonality monitoring
Outcomes
- Fewer Unplanned Breakdowns ↓ 35%
- Average Failure Warning → 14 days
- Maintenance ROI ↑ 3x
What does it connect to?
- CMMS and EAM: SAP PM, IBM Maximo, Oracle EAM, Fiix and UpKeep through REST APIs, with work orders created, updated with findings and closed with evidence
- Accelerometers (100 Hz to 20 kHz), current transformers and thermal cameras, including existing sensors where they fit
- Parts and labor forecasting from the predicted failure type
- Trend dashboards per asset with health scores and remaining-useful-life hints
How is it deployed, and how long does it take?
Fully on-prem with edge gateways and local CMMS integration is supported and common; cloud sync is optional, for model updates and cross-site analytics. Initial tuning happens in the proof of concept and pilot; after that, quarterly reviews.
The first quarter is the honest one: forecasts improve as the models see your assets through a full seasonal cycle, and we report the precision as it moves rather than a brochure figure.
KPI-gated rollout in 8 to 12 weeks. Customers typically see 35 percent fewer unplanned breakdowns within 90 days, with an average failure warning of 14 days.
Industry context
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