Know a machine will fail before it does.
An IoT-integrated platform that monitors vibration, temperature, current draw and acoustic signatures from production equipment, building machine-specific baseline models and detecting anomalies that precede failures by hours or days. Maintenance teams receive prioritised work orders with failure probability and estimated time-to-failure.
Baselines learned per asset. Not assumed from a generic model.
Our IoT background means integration with existing sensor infrastructure happens without ripping and replacing what manufacturers already have installed, and baselines are learned from the specific machine, not applied from a generic template.
- IngestionSensor-agnostic · existing or new IoT
- BaselinesLearned per asset, not assumed from generic models
- Failure modesBearing wear · misalignment · imbalance · electrical
- ScoringProbability + confidence + days-to-failure
- Work ordersAuto-generated · CMMS & ERP integrated
- MobileWork orders, asset history and alerts in one view
- ROI windowMeasurable downtime reduction from Day 90
CNC-014SPINDLE
96
Health
PUMP-07BEARING
61
Health
COMP-03MOTOR
91
Health
▲Anomaly · PUMP-07conf 0.89
Failure mode
Bearing wear
Probability
87%
Est. time
~3.5 days
WO-20418 · Inspect & replace bearing→ DISPATCHED · CMMS
FIG · 02 / Asset health · anomaly detection// CONDITION-BASED · IoT