Predictive Maintenance AI Engine
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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

1

Maintenance scheduling was largely reactive and based on static intervals

2

Unexpected equipment failures triggered costly emergency labor and downtime

3

Historical service records were underused in operational planning

4

Spare parts and intervention planning lacked predictive prioritization

5

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

01

Data Consolidation

Merged historical maintenance records with live IoT telemetry streams.

02

Model Engineering

Developed and validated forecasting and classification models for failure risk.

03

System Integration

Embedded predictions into planning cycles, alerts, and work-order orchestration.

04

Adoption & Optimization

Refined thresholds and interventions through phased rollout and KPI monitoring.

"Predictive scoring changed our maintenance culture from firefighting to planned reliability."
Reliability Engineering Manager
Industrial Utilities Operator

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