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Documentation Index

Fetch the complete documentation index at: https://docs.appliedaifoundation.org/llms.txt

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Predictive maintenance table showing 16 lamps with RUL hours, failure probability, and efficiency

What this tab shows

The Predictive Maintenance tab surfaces pre-computed ML predictions for every UV lamp. It helps engineers prioritise which lamps to replace at the next port call and quantifies the cost benefit of acting on predictions rather than waiting for failures. Data is fetched from /api/predictions?limit=100. The tab filters to component_type === 'UV_LAMP', deduplicates to the latest prediction per lamp by comparing timestamps, then sorts by failure_probability descending so the most at-risk lamps appear first.

Summary stat row

StatDescription
Next MaintenanceDays until the next scheduled maintenance event
At Risk ComponentsCount of lamps with failure_probability ≥ 0.5
Est. SavingsAnnual cost reduction from predictive vs reactive maintenance
Avg RULMean remaining useful life (hours) across all 16 lamps

Component predictions table

Each row represents one UV lamp with four columns:
ColumnSource fieldDescription
Componentcomponent_idLamp name (LAMP_01 through LAMP_16) with colour-coded risk dot
RULpredictions.remaining_useful_life_hoursPredicted hours of useful life remaining
Failure Risk %predictions.failure_probability × 100Predicted probability of failure
Efficiency %predictions.efficiency_percentCurrent lamp efficiency from the prediction model
Statuscurrent_state.statusOPERATIONAL, DEGRADED, or FAILED

Risk thresholds

Failure probabilityRisk levelRow colour
≥ 70%CriticalRed
50–69%HighOrange
30–49%ModerateYellow
< 30%GoodGreen

Upcoming Maintenance section

Three upcoming maintenance tasks are shown with urgency indicators:
TaskInterval
UV Lamp Replacement (priority lamps)Based on RUL predictions
Filter Cleaning (backflush service)Scheduled interval
LDC Fan ServiceScheduled interval
In the demo dashboard, maintenance schedules are illustrative. In a production deployment these should be derived from the RUL values and integrated with your maintenance management system.

Cost Comparison panel

Bar chart comparing reactive maintenance cost of $18,500 against predictive maintenance cost of $5,000
A bar chart compares two maintenance strategies:
StrategyAnnual costAssumptions
Reactive$18,500Emergency lamp replacements, unplanned downtime, expedited parts
Predictive$5,000Bulk replacement during planned port calls, standard lead times
Savings$13,500 (73%)
The cost model illustrates the financial case for acting on RUL predictions. Actual savings depend on vessel schedule and parts procurement.

How to interpret RUL

  • RUL = 0: Failure is predicted to be imminent. Replace at the earliest opportunity.
  • RUL = 500h: At a 3-minute telemetry interval (20 readings/hour), approximately 10,000 more readings before predicted failure — roughly 25 days of continuous operation.
  • RUL = 2,500h on a new lamp: Lamp is at approximately 17% of rated life consumed. No action needed.

Source

  • /api/predictions?limit=100 — all ML predictions
  • Client-side deduplication and sorting (no secondary API call for sorting)