AI Transformer Health Monitoring

Predictive analytics for transformer condition monitoring and maintenance

The Problem

“Prevent transformer failures with predictive health monitoring”

Organizations face these key challenges:

Sparse, manual, and delayed condition data (e.g., quarterly/annual oil tests) that misses rapid deterioration between inspections

Siloed datasets (SCADA, DGA labs, maintenance logs, relay events) and inconsistent data quality across substations and vendors

Reactive maintenance and poor prioritization that leads to emergency outages, long lead times for replacements, and high operational risk

Impact When Solved

20–40% fewer catastrophic transformer failures through early anomaly detection and risk scoring10–20% reduction in maintenance costs by shifting from time-based to condition-based work planning1–4+ hours of avoided outage duration per prevented event, improving reliability metrics and reducing regulatory/penalty exposure

The Shift

Real-World Use Cases

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