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Preventing Transformer Failures with Predictive Asset Health Intelligence
Challenge – Unexpected Failures and Rising Maintenance Costs
A leading utility provider managing over 200 substations faced frequent unexpected transformer failures and escalating operations and maintenance (O&M) costs.
Limited visibility into asset health, fragmented condition data, and reactive maintenance practices led to forced outages, costly emergency repairs, and suboptimal spares management.
Solution – Predictive Asset Health Platform with Nova and AI Analytics
Partnering with Nuodata, the utility implemented an advanced Predictive Asset Health platform designed to monitor, model, and prevent transformer failures.
The solution leveraged:
Nova (Nuodata) and the Asset Health Feature Store for unified data engineering
Integration of thermal, dissolved gas analysis (DGA), and operational features to assess equipment health
Survival analysis models to predict failure probability and optimize maintenance intervals
Work order prioritization to focus efforts on high-risk assets
This data-driven ecosystem enabled proactive asset monitoring, intelligent maintenance planning, and reduced unplanned downtime across substations.
Impact – Fewer Failures, Smarter Maintenance
The initiative delivered measurable reliability and cost-saving outcomes:
22% reduction in forced outages across substations
30% decrease in avoidable transformer failures through predictive maintenance
Improved spares planning lead time, optimizing inventory and field readiness
Enhanced grid reliability and asset lifecycle management
With predictive analytics and real-time asset intelligence, the utility transformed from reactive maintenance to proactive reliability management, driving operational excellence and cost efficiency.
Meta Description
See how a utility provider used Nuodata’s Nova platform to build a predictive asset health system—reducing transformer failures by 30%, cutting forced outages by 22%, and improving spares planning with AI-driven insights.
