Machine Learning
Cubico · Renewable Energy
Predictive Maintenance Across 12,000+ SCADA Sensors
−28% unplanned downtime
91% detection precision
3–7 days early warning
The Challenge
12,000+ SCADA sensors generating continuous telemetry with no system to identify degradation patterns before failure. Faults were only discovered after breakdown — with intervention costs substantially higher and renewable energy output directly impacted.
What We Built
A high-frequency anomaly detection pipeline processes all 12,000+ sensors continuously. ML models trained on healthy operating baselines surface failure indicators 3–7 days before breakdown occurs. Maintenance work orders are automatically raised and priority-scored — teams act before equipment fails.
Operational Impact
28% year-on-year reduction in unplanned equipment failures. 91% detection accuracy with less than 4% false positive rate. £2.1M in emergency intervention costs avoided in Year 1.
Technology Used
Python
PyTorch
scikit-learn
Azure ML
Grafana
PostgreSQL
