Health Index

A robust index must ingest heterogeneous data streams to avoid single-point diagnostic blindness. It synthesizes Dissolved Gas Analysis (DGA) for thermal and electrical faults, oil quality tests (moisture, acidity, dielectric strength) for insulation aging, and operational history (load profiles, fault events). Advanced implementations also incorporate Furan Analysis to directly quantify solid insulation degradation and Infrared Thermography hotspots. The fusion of electrical, chemical, and thermal data provides a holistic view that no single test can offer.

Not all diagnostic inputs are equally predictive of failure. A rigorous index applies expert-derived or data-driven weighting to each parameter. For example, Degree of Polymerization (DP) of paper insulation carries a higher weight than oil color because it directly measures mechanical integrity. Weights are often derived from Weibull Distribution analysis of historical failure data or through Explainable AI (XAI) techniques like SHAP values that quantify each feature's contribution to past failures. This ensures the composite score reflects true risk rather than averaging trivial anomalies with critical ones.

A static snapshot is insufficient. A robust Health Index incorporates rate-of-change calculations and time-series forecasting to detect accelerating degradation before a threshold breach. For instance, a sudden increase in hydrogen generation rate signals active partial discharge, even if absolute ppm levels remain below alarm limits. Models like LSTM or Temporal Fusion Transformer predict future gas trajectories, enabling the index to reflect not just current condition but projected near-term risk. This transforms the index from a reactive indicator into a predictive prognostic tool.

A single aggregate score can mask distinct failure mechanisms. A well-designed index decomposes into sub-indices mapped to specific Failure Mode Classifications:

• Thermal fault index: Driven by ethylene and methane ratios
• Electrical fault index: Driven by acetylene and hydrogen
• Insulation aging index: Driven by furans, DP, and moisture This granularity allows asset managers to distinguish between a transformer with severe insulation aging but no active electrical fault and one with a dangerous arcing condition, enabling targeted maintenance decisions.

Raw sensor values must be normalized against physics-based limits defined in standards like IEC 60599 and IEEE C57.104. A robust index does not use arbitrary min-max scaling. Instead, gas concentrations are scored relative to their 90% typical values and absolute fault thresholds. Hot-spot temperature calculations per IEEE C57.91 are integrated to normalize load-dependent thermal stress. This ensures that a score of '80' means the same degree of degradation across different transformer designs, voltage classes, and operating environments, enabling fleet-wide comparison.

Every diagnostic measurement carries inherent uncertainty from sensor drift, sampling procedures, or laboratory accuracy. A mature Health Index does not output a single deterministic number. It provides a confidence interval or probability distribution. For example, if an Online DGA Monitor exhibits sensor drift, the index should widen its uncertainty bounds rather than silently propagating erroneous data. Techniques like Bayesian inference or ensemble learning aggregate multiple models to produce both a point estimate and a variance, allowing reliability engineers to make risk-informed decisions under uncertainty.

The definitive chemical metric for solid insulation aging. DP measures the average cellulose chain length in transformer paper; new insulation starts at 1000–1200 DP, with end-of-life declared at 200 DP.

• Directly correlates to tensile strength of winding insulation
• Irreversible degradation driven by heat, moisture, and oxygen
• Combined with furan analysis to estimate DP non-invasively from oil samples

A prognostic output derived from the health index that estimates operational time remaining before functional failure. RUL models consume condition scores and project degradation trajectories.

• Uses Weibull distribution or exponential degradation models
• Enables capital planning and spare transformer procurement
• Distinct from health index: HI describes current state, RUL forecasts future state

The operational philosophy that health indices enable. Rather than time-based maintenance cycles, CBM triggers interventions only when diagnostic evidence indicates degrading condition.

• Reduces unnecessary outages and maintenance costs
• Relies on continuous monitoring inputs: online DGA, infrared thermography, partial discharge sensors
• Health index thresholds define maintenance decision boundaries

The international normative reference for interpreting dissolved gas analysis in mineral oil-filled equipment. Defines typical gas concentration limits, key gas ratios, and fault type classification rules.

• Establishes 90% typical concentration values for healthy transformers
• Defines ratio codes: C₂H₂/C₂H₄, CH₄/H₂, C₂H₄/C₂H₆
• Forms the rule-based layer within hybrid health index models

Machine learning methodology that combines multiple predictive models—Random Forest, XGBoost, Gradient Boosting—to improve diagnostic accuracy over single-algorithm approaches.

• Aggregates predictions via voting or weighted averaging
• Reduces overfitting and improves generalization across transformer populations
• Commonly used to classify failure modes from DGA and electrical test data