Industrial Twin Lab

Build machine intelligence in the twin before trusting it in the machine.

An isolated experimentation environment for digital twins, industrial AI, simulation, and evidence-driven machine intelligence.

The operating thesis

Figure 01

  1. Physical assetReality
  2. Digital twinRepresentation
  3. Isolated experiment labInquiry
  4. AI scientistReasoning
  5. Evidence packageDecision

Never let AI perform its first experiment on the physical machine.

  • Twin before intervention
  • Human in command
  • Evidence before deployment

Selected principles / 12 total

A discipline for machine intelligence

  1. Twin Before Intervention

  2. Physics Before Pure Correlation

  3. Evidence Before Deployment

Demonstration asset

P-101 — Boiler Feed Water Pump

One fictional but engineering-realistic centrifugal pump connects the manifesto, Twin Capsule, experiments, fault laboratory, and AI Scientist. It is a synthetic teaching fixture, not a physical installation or source of plant evidence.

P-101 Twin Capsule

TWIN-P101-0.1.0

Asset identity

Asset ID
P-101
Asset
Boiler Feed Water Pump P-101
Type
Centrifugal Pump
Driver
Electric Motor
Description
Fictional centrifugal boiler feed water pump used only as a consistent Industrial Twin Lab demonstration asset.
Hierarchy
Enterprise → Fleet → Plant → System → Machine → Component
service
Boiler feed water
configuration
Single-stage centrifugal pump with electric-motor drive
designFlow
240 m³/h
designHead
112 bar

Features

  • pressure-ratioPressure RatioDischarge pressure relative to suction pressure.process; sources: suction-pressure, discharge-pressure
  • flow-per-speedFlow / SpeedFlow normalized by rotational speed.process; sources: flow, speed
  • power-per-flowPower / FlowElectrical power normalized by flow.process; sources: motor-power, flow
  • bearing-de-delta-ambientDE temperature delta ambientDrive-end bearing temperature above ambient.physics; sources: bearing-de-temperature, ambient-temperature
  • bearing-nde-delta-ambientNDE temperature delta ambientNon-drive-end bearing temperature above ambient.physics; sources: bearing-nde-temperature, ambient-temperature
  • vibration-rmsVibration RMSRMS vibration from bearing-housing measurements.vibration; sources: axial-vibration, radial-vibration
  • vibration-kurtosisVibration KurtosisDistribution-tail indicator for vibration change.vibration; sources: axial-vibration, radial-vibration
  • twin-residualTwin ResidualMeasured value minus digital-twin prediction.physics; sources: motor-power, flow, speed
  • rolling-mean-30mRolling Mean 30mThirty-minute rolling temperature mean.temporal; sources: bearing-de-temperature

Signals

Signal IDSignalUnitQuantityLocationNominal fixture value
suction-pressuresuction pressurebarPressurePump suction2.6 bar
discharge-pressuredischarge pressurebarPressurePump discharge114 bar
flowflowm³/hVolumetric flowDischarge line240 m³/h
motor-currentmotor currentAElectrical currentMotor control center168 A
motor-powermotor powerkWElectrical powerMotor control center101 kW
speedspeedrpmRotational speedMotor shaft2950 rpm
bearing-de-temperaturebearing DE temperature°CTemperatureDrive-end bearing68 °C
bearing-nde-temperaturebearing NDE temperature°CTemperatureNon-drive-end bearing64 °C
axial-vibrationaxial vibrationmm/s RMSVibration velocityBearing housing1.9 mm/s RMS
radial-vibrationradial vibrationmm/s RMSVibration velocityBearing housing2.4 mm/s RMS
ambient-temperatureambient temperature°CTemperaturePump enclosure28 °C

Operating envelope

flowMinimum
180 m³/h
flowMaximum
280 m³/h
suctionPressureMinimum
2.2 bar
dischargePressureMaximum
125 bar
ambientTemperatureMinimum
5 °C
ambientTemperatureMaximum
45 °C

Failure modes

  • bearing-degradationbearing degradationProgressive degradation of bearing condition.Affected signals: bearing-de-temperature, bearing-nde-temperature, axial-vibration, radial-vibration
  • impeller-degradationimpeller degradationLoss of hydraulic performance from impeller condition.Affected signals: flow, motor-power, discharge-pressure
  • cavitationcavitationVapour-cavity formation associated with inadequate suction conditions.Affected signals: suction-pressure, axial-vibration, radial-vibration
  • suction-restrictionsuction restrictionRestriction upstream of the pump inlet.Affected signals: suction-pressure, flow, motor-power
  • seal-leakageseal leakageLoss of process fluid at the pump seal.Affected signals: flow, discharge-pressure
  • motor-degradationmotor degradationReduced motor efficiency or electrical condition.Affected signals: motor-current, motor-power, speed

Model and uncertainty boundary

Physics model implementation
Not implemented
Model validation
Not validated in Phase 1
Availability statement
No physics model is implemented or validated in Phase 1.
Uncertainty status
Unquantified
Uncertainty statement
Uncertainty is unquantified because P-101 has no plant measurements or validated industrial evidence.

Limitations

  • P-101 values and relationships are synthetic teaching fixtures, not plant measurements or validated industrial evidence.
  • Read-only conceptual fixture; no operational technology connection exists.
  • No result authorizes automatic control or setpoint changes.

Safety constraints

  • Read-only conceptual fixture; no operational technology connection exists.
  • No result authorizes automatic control or setpoint changes.
  • Human engineering review is required before any physical-machine decision.

Provenance and disclosure

Asset version
ASSET-P101-0.1.0
Twin version
TWIN-P101-0.1.0
Source
Industrial Twin Lab fictional engineering fixture
Origin
Fictional / synthetic fixture

P-101 values and relationships are synthetic teaching fixtures, not plant measurements or validated industrial evidence.

A versioned fictional machine record with eleven named signals, six failure modes, explicit safety constraints, and source-aware provenance.

P-101 Evidence Package

EXP-P101-BD-COMBINED-XGBOOST-WALKFORWARD

Conceptual demonstration — synthetic fixture results.

Experiment record

Model
MODEL-XGB-0.1.0
Model status
experimental
Dataset
DATASET-P101-SYN-0.1.0
Feature set
combined
Validation
walk-forward
Twin version
TWIN-P101-0.1.0
Asset version
ASSET-P101-0.1.0
Dataset version
DATASET-P101-SYN-0.1.0
Simulator version
SIM-P101-0.1.0
Feature pipeline
FEATURES-P101-0.1.0
Provenance model
MODEL-XGB-0.1.0
Code version
ITL-PHASE-1-0.1.0
Configured asset
P-101
Problem
bearing-degradation
Configured feature set
combined
Algorithm
xgboost
Configured validation
walk-forward
Random seed
101
Timestamp label
Synthetic fixture

Qualified evidence

EvidenceSummaryStrengthOrigin
EXP-P101-BD-COMBINED-XGBOOST-WALKFORWARD-SYNTHETIC-EVIDENCEDeterministic synthetic fixture for conceptual comparison.limitedSynthetic fixture

Metric results

MetricResult
Detection Rate86 %
False Alarms0.9 alerts/month
Lead Time5 days
Inference Cost18 ms
Sensor Count11 sensors
Robustness80 /100
Explainability78 /100

Operating regimes

  • Nominal flow: 220–260 m³/h
  • Rated speed: 2,900–3,000 rpm
  • Ambient temperature: 5–45 °C

Limitations

  • Synthetic fixture results do not establish plant performance.
  • No control decision, alarm threshold, or maintenance action is authorized.
  • Simulation fidelity and transferability require independent engineering validation.

Uncertainty, explainability, and provenance

Uncertainty
Illustrative uncertainty only; no confidence value is derived from an operating machine.
Explainability
XGBoost compared under Walk Forward.
Dataset source
Industrial Twin Lab synthetic experiment fixture
Dataset disclosure
This dataset is a deterministic synthetic fixture for conceptual comparison only; it is not plant data.
Experiment source
Industrial Twin Lab deterministic experiment fixture lookup
Experiment disclosure
Conceptual demonstration — synthetic fixture results. No model is trained or executed and no real plant data is used.
Author agent
Industrial Twin Lab synthetic fixture agent

Human engineer retains decision authority. This package does not authorize control, maintenance action, or safety certification.

A deterministic conceptual package for review, not production evidence or authorization to intervene.

Evidence-backed machine knowledge.

The primary output is defensible machine knowledge: a reproducible body of evidence that an engineer can inspect, challenge, and govern—not a model score or autonomous act.

Continue to the manifesto