Evidence before inference
Usage, confirmed failure events, completed PMs, overdue planned work, and sensor exceptions are stored as versioned feature snapshots.
Phase 9 · Predictive analytics
See risk.
Show your work.
Revolution Road turns maintenance history and condition signals into explainable risk evidence while leaving maintenance decisions with people.
Trust architecture
Useful because it is inspectable.
The first active model is deliberately simple. It establishes clean data, measurable outcomes, and user feedback before a supervised model earns production authority.
Reasons beside every score
Each risk result names its strongest contributors, their values, and their weighted effect. Missing evidence reduces confidence.
People remain in control
Accept, reject, or defer a suggestion. Revolution Road records the decision for evaluation but never silently rewrites a PM schedule.
Promotion gates
“AI” is a result, not a label.
A supervised time-to-failure candidate runs in shadow until it can beat the baseline on connected historical data without drifting beyond agreed thresholds.
Clean history
Versioned features, confirmed failure labels, and no PII in the training export.
Backtest
At least 30 matured predictions, 5 failures, and reported precision, recall, and errors.
Shadow
Compare candidate and active scores without letting the candidate affect field work.
Human approval
Named activation, checksum evidence, rollback path, and no automatic PM edits.
Methodology whitepaper