Predictive maintenance — Power plant
Vibration, current and thermal models that predict failures days ahead. Delivered for a power plant client in Rajkot.
- Location
- Rajkot
- Year
- 2020
- Duration
- 33 weeks
- Team
- 10 engineers
- 31%
- fewer breakdowns
- 6.6
- days warning
- −17%
- maintenance cost
The challenge
Data existed but lived in spreadsheets and legacy systems, so decisions were made on instinct rather than evidence.
What we built
We owned the whole stack — hardware, firmware, models and dashboards — using scikit-learn, XGBoost, InfluxDB so there was one accountable team.
Delivery timeline
Phase 1
Discovery sprint and site survey
Phase 2
Prototype built and tested in the Motera lab
Phase 3
Pilot on site with before/after measurement
Phase 4
Scale-out, training and handover
Highlights
- Operator training delivered in Gujarati, Hindi and English
- Integrated with existing PLC, ERP or GIS systems — no rip-and-replace
- Runs on-premises; no data leaves the site unless agreed
- Documentation, drawings and source handed over at close
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