When Monitoring Data Misleads: The Case for Correlative Asset Analysis

Why a Multi-Million Dollar Transformer Failed Despite "Normal" Monitoring—And How Correlative Analysis Would Have Prevented It

You have monitoring systems generating alarms. But when one triggers, can you confidently answer: "What does this actually mean for my asset?" Most utilities can't—and it's costing millions in unnecessary interventions and missed catastrophic failures.

The Critical Flaw in Traditional Monitoring

Since deregulation privatized the power industry, utilities shifted to profit-driven asset optimization. Monitoring systems proliferated, but created a new problem: scattered data without context. Parameters analyzed independently—DGA here, partial discharge there, temperature elsewhere—lead to "false alarms" that destroy trust and real faults that slip through undetected.

What This Case Study Reveals:

Analytical Models That Extract Intelligence – Discover how leading utilities transform raw monitoring data through abstraction layers into unified health assessments using severity analysis and correlative techniques that increase assessment confidence.

The Multi-Parameter Advantage – Learn why monitoring the same failure mechanism with multiple analytical models (DGA, Core Ground Current, Gas Accumulation Rate, Thermal Models, PD) catches faults that single-parameter monitoring misses—each with different detection timeframes.

Documented Catastrophic Failure – Review the single-phase EHV autotransformer case equipped with comprehensive monitoring: DGA, bushing sensors, temperature monitoring, plus 6 UHF partial discharge sensors. After 3 months in service, it failed catastrophically. DGA showed nothing. Bushing monitors showed nothing. But PD detected strong activity 8 hours before failure—proving different parameters don't just support each other, they complement by covering different failure dynamics.

From Data Overload to Confident Decisions – Understand how correlative analysis reveals when multiple independent parameters support the same conclusion—or when contradictions signal deeper investigation is needed before costly interventions.

Download this case study to move from monitoring confusion to intelligent asset management.