Remaining Useful Life (RUL) Prediction

How per-lamp failure probability and RUL hours are predicted and surfaced in the dashboard.

What RUL is

Remaining Useful Life is the predicted number of operating hours before a UV lamp is expected to fail or require replacement. It is stored in the bwts_iot_predictions table and surfaced in the Predictive Maintenance tab.

A lamp with an RUL of 0 should be treated as imminently failed. A lamp with an RUL of 2,800 hours has most of its rated 3,000-hour life still ahead of it.

The prediction record schema

Each prediction record contains:

{
  "timestamp": "2026-04-30T00:00:00Z",
  "component_id": "LAMP_01",
  "component_type": "UV_LAMP",
  "predictions": {
    "remaining_useful_life_hours": 450,
    "failure_probability": 0.62,
    "efficiency_percent": 81.3
  },
  "current_state": {
    "runtime_hours": 2150,
    "efficiency_percent": 81.3,
    "status": "OPERATIONAL"
  }
}

Field	Description
component_id	Lamp identifier, e.g. LAMP_01 through LAMP_16
component_type	Always UV_LAMP for lamp predictions
predictions.remaining_useful_life_hours	Predicted hours of useful life remaining
predictions.failure_probability	Float 0.0–1.0; probability of failure within the prediction horizon
predictions.efficiency_percent	Predicted efficiency from the model
current_state.runtime_hours	Cumulative hours the lamp has operated
current_state.status	OPERATIONAL, DEGRADED, or FAILED

How predictions are generated

Predictions are pre-computed by a separate ML pipeline — not by the dashboard. The pipeline analyses each lamp’s runtime history and efficiency degradation curve and writes prediction records to the database. The dashboard reads results via /api/predictions.

Dashboard deduplication logic

The /api/predictions endpoint may return multiple prediction records per lamp (one per prediction run). The Predictive Maintenance tab applies this deduplication:

1. Fetch up to 100 prediction records
2. Filter to component_type === 'UV_LAMP'
3. For each component_id, keep only the record with the most recent timestamp
4. Sort remaining records by failure_probability descending (most at-risk first)

This ensures exactly one row per lamp regardless of how many historical prediction records exist.

Failure probability thresholds

Failure probability	Risk level	Row colour	Action
≥ 70%	Critical	Red	Plan replacement at next port call
50–69%	High	Orange	Elevated risk — schedule replacement within 2 port calls
30–49%	Moderate	Yellow	Monitor — include in next routine inspection
< 30%	Good	Green	No action needed

Lamp lifetime reference

Threshold	Value	Source
Rated lifetime	3,000 hours	THRESHOLDS.LAMP_RUNTIME.MAX
Maintenance warning	2,500 hours	THRESHOLDS.LAMP_RUNTIME.WARNING

When current_state.runtime_hours approaches 2,500h, failure probability rises sharply on the degradation curve. Lamps beyond 2,500h with failure_probability ≥ 0.5 should be treated as high priority for replacement.

Cost implication

The Predictive Maintenance tab’s cost comparison ($18,500 reactive vs$5,000 predictive annually) is based on the following logic:

• Reactive: Emergency lamp replacements require expedited parts, unplanned dry-dock time, and elevated labour costs
• Predictive: Scheduling replacements based on RUL during planned port calls allows bulk procurement and standard crew schedules

References

• Health Score Calculation — how lamp health feeds into the composite score
• Predictive Maintenance Tab — where this data is displayed
• /api/predictions — the endpoint serving this data