Documentation Index
Fetch the complete documentation index at: https://docs.appliedaifoundation.org/llms.txt
Use this file to discover all available pages before exploring further.
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:
- Fetch up to 100 prediction records
- Filter to
component_type === 'UV_LAMP'
- For each
component_id, keep only the record with the most recent timestamp
- 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 |
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 (18,500reactivevs5,000 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
