Predictive Maintenance Tab

ML-based failure probability, remaining useful life per lamp, and scheduled maintenance cost analysis.

Predictive Maintenance Tab — component predictions sorted by failure risk

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

Stat	Description
Next Maintenance	Days until the next scheduled maintenance event
At Risk Components	Count of lamps with failure_probability ≥ 0.5
Est. Savings	Annual cost reduction from predictive vs reactive maintenance
Avg RUL	Mean remaining useful life (hours) across all 16 lamps

Component predictions table

Each row represents one UV lamp with four columns:

Column	Source field	Description
Component	component_id	Lamp name (LAMP_01 through LAMP_16) with colour-coded risk dot
RUL	predictions.remaining_useful_life_hours	Predicted hours of useful life remaining
Failure Risk %	predictions.failure_probability × 100	Predicted probability of failure
Efficiency %	predictions.efficiency_percent	Current lamp efficiency from the prediction model
Status	current_state.status	OPERATIONAL, DEGRADED, or FAILED

Risk thresholds

Failure probability	Risk level	Row colour
≥ 70%	Critical	Red
50–69%	High	Orange
30–49%	Moderate	Yellow
< 30%	Good	Green

Upcoming Maintenance section

Three upcoming maintenance tasks are shown with urgency indicators:

Task	Interval
UV Lamp Replacement (priority lamps)	Based on RUL predictions
Filter Cleaning (backflush service)	Scheduled interval
LDC Fan Service	Scheduled 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

Cost comparison — reactive vs predictive maintenance

A bar chart compares two maintenance strategies:

Strategy	Annual cost	Assumptions
Reactive	$18,500	Emergency lamp replacements, unplanned downtime, expedited parts
Predictive	$5,000	Bulk 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)