Comparison

Compare time-based, condition-based, and AI-assisted maintenance planning

A practical comparison of time-based maintenance, condition-based maintenance, and human-reviewed AI-assisted CBM for transformer fleets.

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Time-based maintenance, condition-based maintenance, and AI-assisted CBM each have a role. The practical question is whether evidence is strong enough, reviewable enough, and governed enough to support better work-package decisions.

Verdict

Where each approach fits.

No tool wins every scenario. Use these signals to decide whether a GridAPM pilot is worth your team's time.

Choose GridAPM when

Teams moving from calendar-only maintenance to evidence-led transformer review.
Asset managers who want better evidence packs before changing maintenance intervals.
Utilities that need safe AI assistance without autonomous maintenance authority.

Choose another path when

The organization lacks minimum evidence provenance or reviewer availability.
The team wants AI to override maintenance policy.
The pilot cannot define a measurable workflow or asset population.

See the shift

From fixed calendars to condition-triggered work.

Every 12 months

Fixed calendar intervals: healthy units are opened anyway, and developing faults wait for the next slot.

When the evidence says so

Condition-triggered interventions: a DGA trend shift or PD onset opens one targeted work package.

Blend between calendar-based and condition-based maintenance

25–30%

reduction in maintenance costs

35–45%

reduction in downtime

US DOE Federal Energy Management Program, O&M Best Practices Guide (PNNL-14788) — cross-industry predictive-maintenance program averages, not GridAPM measurements.

Comparison table

Where GridAPM changes the workflow.

Use this table to frame a pilot conversation around evidence, governance, and work-package quality rather than broad software categories.

TBM vs CBM vs AI-Assisted CBM for Transformers — Row-by-row comparison. Claims stay bounded to workflow fit and evidence handling.
Criterion	TBM / CBM baseline	AI-assisted CBM (GridAPM pilot)
Maintenance trigger	TBM uses calendar, interval, or policy cycles. CBM uses condition evidence and review thresholds.	AI-assisted CBM drafts evidence summaries and questions, but approval remains human-reviewed.
Evidence burden	TBM needs less diagnostic evidence but may miss changing condition. CBM needs more source quality.	GridAPM helps identify missing evidence, provenance gaps, and review-readiness issues.
Governance	TBM governance is policy-based. CBM governance depends on diagnostic review discipline.	AI-assisted CBM needs explicit source boundaries, audit trails, and approval paths.
Best pilot metric	TBM compares schedule adherence. CBM compares review quality, risk prioritization, and evidence coverage.	AI-assisted CBM pilots should measure preparation effort, traceability, reviewer confidence, and work-package quality.

Next steps

Keep the evaluation moving.

Evidence readiness checklist

Check whether your DGA, PD, SFRA, and inspection records can support CBM.

CBM pilot scorecard

Baseline and measure a CBM pilot before changing maintenance intervals (PDF).

Pilot evaluation

The eight-week pilot structure: scope, evidence inputs, and deliverables.

FAQ

Comparison without inflated claims.

Is AI-assisted CBM better than TBM?

Not automatically. AI-assisted CBM is useful only when evidence quality, reviewer workflow, and governance are strong enough for safe review.

Can GridAPM decide maintenance timing?

No. GridAPM can help prepare evidence and draft review material; maintenance timing and approval remain with qualified personnel.

Test the workflow on your own fleet.

An eight-week pilot runs on your approved evidence, with your engineers keeping sign-off authority at every step.

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