Design an actionable predictive-maintenance alert workflow for an autonomous fleet.

Example answer

An alert should carry vehicle/component identity, model and data version, observation time, relevant evidence, estimated uncertainty and the time window in which a useful intervention is possible. Define eligibility and data freshness before scoring; an expired or unsupported prediction should not masquerade as a current assessment.

Assign an accountable owner, acknowledgement deadline and escalation path. Separate risk triage from the maintenance schedule, accounting for consequence, evidence quality, service capacity and operational constraints. A high score does not automatically prove a defect, and model ranking does not authorize a change to driving controls.

Inspection records should distinguish confirmed faults, no fault found, unresolved cases and preventive replacement. Record what happened after an alert as well as cases with no alert. Otherwise evaluation overcounts successful interventions and ignores false negatives or censoring from early replacements.

Return to service requires the appropriate diagnostic and configuration checks, not merely clearing the dashboard. An unacknowledged alert needs an explicit state and escalation rather than silent expiry into healthy status.

Measure useful lead time, missed failures, unnecessary inspections, queue delay and completed interventions alongside downtime and cost. Compare with the existing maintenance policy using an appropriate evaluation design. The workflow makes predictions actionable; it does not guarantee that all failures can be prevented.