DGA trend
H2 and CH4 movement reviewed against operating context
NormalSample evidence pack
Transformer condition review, packaged for human decision
This sample shows the type of pilot-ready evidence pack GridAPM Ai can produce: source-linked evidence, AI rationale, lifecycle context, engineer review, and a clear next action.
Transformer T-2 82/100
Asset sustainability condition
Healthy, continue lifecycle monitoring
Sample status only. This page demonstrates report structure and review flow; it does not claim real operating results.
Moisture in oil
Moisture trend and oil quality indicators remain within review band
NormalPRPD activity
Pattern activity requires continued monitoring and noise-context review
MonitorThermal model
Hot-spot estimate and cooling state reviewed for lifecycle impact
MonitorMaintenance record
Follow-up inspection remains open for next planned outage
ActionEngineer signoff
Recommendation reviewed and accepted for continued monitoring
ApprovedReport anatomy
What a GridAPM evidence pack should make reviewable
Buyers should be able to inspect the evidence, understand why the recommendation was drafted, see who reviewed it, and preserve the decision record for future maintenance planning.
Asset context
Transformer T-2, critical substation asset, approved pilot dataset, maintenance and diagnostic history available.
AI rationale
Agentic AI surfaces that DGA and moisture are stable, PRPD remains a monitoring item, and thermal context should stay visible in lifecycle planning.
Human review
Engineer reviews source evidence, confirms assumptions, documents rationale, and approves continued monitoring with defined follow-up.
Lifecycle note
Avoid immediate replacement framing; preserve useful-life decision context and reassess after next diagnostic cycle.
Pilot-ready proof
Use the sample as a review template
During a real pilot, this structure should be populated with approved customer evidence, agreed review criteria, and the decision workflow your engineering team wants to evaluate.
Source evidence and provenance
AI rationale and assumptions
Health index and lifecycle context
Engineer review and signoff
Next action and decision log