A discipline comprising data‑driven and physics‑based methods that monitor asset condition, estimate current degradation, predict remaining useful life (RUL) with quantified uncertainty, and inform maintenance and operational decisions for power‑system equipment.

Principle

Combining condition monitoring data with degradation models (statistical, machine‑learning or physics‑of‑failure) yields probabilistic estimates of future performance and RUL; decisions follow by mapping those probabilistic forecasts to maintenance, spare‑parts and operational cost or risk criteria.

Demonstration

Illustrative application — Situation: A substation transformer exhibits rising dissolved gas concentrations and incremental temperature trends. Recognition: A model calibrated to historical degradation correlates the trends with insulation aging. Action: The PHM system outputs a probabilistic RUL and proposes a maintenance window optimized for minimal service disruption. Consequence: Planned maintenance is scheduled before a high‑risk deterioration period, reducing unscheduled outage likelihood.

Misapplication

Using a point RUL estimate without expressing uncertainty or without validating the model on the same asset class and operating regime; the error is treating model outputs as exact predictions rather than probabilistic forecasts dependent on model validity and data quality.

Consequence

When properly applied, PHM enables condition‑based maintenance that can lower life‑cycle cost and reduce unplanned outages; misapplied PHM can create misplaced confidence, lead to missed failures or unnecessary replacements, and shift risk rather than eliminate it.

Reversal

PHM value is limited when failures are predominantly random and instantaneous (e.g., sudden mechanical breakage) with no measurable precursor signals; in such cases predictive models cannot provide reliable early warning.

Boundary

Clearly within: assets that show measurable, progressive degradation signals (batteries, transformers, rotating machines, power electronics) where sensing and models can be applied. Boundary case: assets with mixed gradual and abrupt failure modes where PHM assists for some failure types but not others. Clearly outside: failures that are purely stochastic with no precursor or decisions based solely on regulatory fixed intervals.

Semantic Tension

Risk Reduction ↔ Cost of Surveillance — increasing monitoring and model sophistication reduces uncertainty but raises instrumentation, data and analytical costs; optimal PHM balances predictive value against these costs.

Synthesis

PHM reframes maintenance as probabilistic decision‑making under uncertainty: it translates sensor signals and models into risk‑graded forecasts that must be integrated with cost, availability and safety criteria to produce actionable maintenance plans.