
Predictive Maintenance AI Engine
Implemented a predictive maintenance platform using historical records and live IoT telemetry to reduce unplanned downtime and improve asset lifecycle outcomes.
-40%
Unexpected Downtime
-25%
Routine Costs
-60%
Emergency Repairs
+15%
Asset Lifespan
Challenge to Solution
How we translated complex operational constraints into a clear implementation path.
The Challenge
Maintenance scheduling was largely reactive and based on static intervals
Unexpected equipment failures triggered costly emergency labor and downtime
Historical service records were underused in operational planning
Spare parts and intervention planning lacked predictive prioritization
Critical asset health visibility was inconsistent across facilities
Our Solution
Unified 10-20 years of maintenance logs with real-time vibration and temperature data
Built LSTM and ensemble models to forecast likely failure windows
Generated failure probability and remaining useful life (RUL) scores
Integrated recommendations into maintenance planning and dispatch workflows
Created dashboards for reliability teams to track risk and intervention outcomes
Project Timeline
Delivery span: 6 months
Data Consolidation
Merged historical maintenance records with live IoT telemetry streams.
Model Engineering
Developed and validated forecasting and classification models for failure risk.
System Integration
Embedded predictions into planning cycles, alerts, and work-order orchestration.
Adoption & Optimization
Refined thresholds and interventions through phased rollout and KPI monitoring.
"Predictive scoring changed our maintenance culture from firefighting to planned reliability."
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