Assets API

Asset CRUD endpoints, hierarchy, health, prognostics, digital twin, baselines, and condition.

CRUD Endpoints

Method	Endpoint	Description
`GET`	`/api/v1/assets`	List assets
`POST`	`/api/v1/assets`	Create asset
`GET`	`/api/v1/assets/{asset_id}`	Get asset
`PUT`	`/api/v1/assets/{asset_id}`	Update asset
`DELETE`	`/api/v1/assets/{asset_id}`	Delete asset

Asset Hierarchy

Assets are organized in a parent-child structure:

POST /api/v1/assets
{
  "name": "Pump Motor P-101",
  "type": "motor",
  "parent_id": "asset_north_plant",
  "location": { "building": "A", "floor": 1, "zone": "pumping" },
  "metadata": { "manufacturer": "ABB", "model": "M3BP-315", "year": 2022 }
}

Query the full hierarchy tree:

GET /api/v1/assets/tree?root_id=asset_north_plant&depth=3

Parameter	Description
`root_id`	Root asset ID (optional)
`depth`	Depth levels (default: 5)

Asset Health

GET /api/v1/assets/{asset_id}/health

Response:

{
  "health_score": 82,
  "status": "good",
  "factors": [
    { "name": "vibration", "score": 75, "trend": "stable" },
    { "name": "temperature", "score": 90, "trend": "improving" },
    { "name": "maintenance_compliance", "score": 80, "trend": "declining" }
  ],
  "last_updated": "2026-03-25T09:30:00Z"
}

Status	Score Range
`critical`	0-25
`poor`	26-50
`fair`	51-70
`good`	71-90
`excellent`	91-100

Prognostics

GET /api/v1/assets/{asset_id}/prognostics

Returns predictions based on machine learning models:

Field	Description
`remaining_useful_life`	Estimate in days
`failure_probability`	Failure probability in 30 days (0-1)
`recommended_action`	Action suggested by the model
`confidence`	Prediction confidence level
`model_version`	Model version used

Digital Twin

GET /api/v1/assets/{asset_id}/digital-twin

Returns the asset's virtual state with real-time data:

Active sensors — Current readings from all associated sensors
Operational parameters — Speed, load, temperature, pressure
Simulations — Results from the last simulated scenario
Detected anomalies — Deviations from the base model

Baselines

Baselines define the expected normal behavior of an asset:

POST /api/v1/assets/{asset_id}/baselines
{
  "parameter": "vibration_rms",
  "baseline_value": 2.5,
  "tolerance_percent": 15,
  "measurement_unit": "mm/s"
}

GET /api/v1/assets/{asset_id}/baselines

When a reading exceeds the defined tolerance, an automatic alert is generated.

Current Condition

GET /api/v1/assets/{asset_id}/condition

Field	Description
`overall_condition`	Overall condition: good, fair, poor, critical
`parameters`	Array of monitored parameters
`last_inspection`	Date of last inspection
`next_inspection`	Recommended next inspection date
`open_work_orders`	Number of open work orders

Condition is recalculated automatically each time a new sensor reading is received or an inspection is completed.