Predictive Maintenance AI Platform
Case Study

The challenge

An energy infrastructure operator managing 120 generation and distribution assets was experiencing increasing unplanned maintenance events. Their maintenance strategy was time-based (fixed inspection schedules) rather than condition-based, leading to both early replacements and missed failures.

What we built

• IoT sensor data ingestion from vibration, temperature, current, and pressure sensors across all 120 assets
• Time-series ML failure prediction models per asset class, trained on 3 years of sensor and maintenance history
• Remaining Useful Life (RUL) estimation with confidence intervals for each monitored asset
• Automated CMMS work order generation when predicted failure probability crosses configurable thresholds
• Maintenance scheduling optimisation to balance predicted risk with available technician capacity

The results

The solution delivered measurable results within weeks of go-live. The client achieved 35% Downtime reduction and 2-4wk Failure prediction lead time, with full ROI achieved within 8 months ROI achieved.