Experiment Fabric
The Experiment Fabric treats an experiment as a first-class, reproducible object rather than a notebook cell or a chart. Its contract connects the engineering question to the exact evidence returned:
Complete experiment anatomy
Asset
Versioned machine identity and twin context.
Problem
Bearing degradation as the bounded failure mode.
Dataset
Synthetic, versioned fixture with explicit provenance.
Operating regime
The envelope in which evidence is interpreted.
Target and horizon
Seven-day detection objective and outcome.
Feature set
Process, vibration, physics, or combined inputs.
Algorithm
Candidates compete under identical conditions.
Configuration
Exact selections, versions, and random seed.
Validation
Time-aware evaluation without future leakage.
Metrics and cost
Performance, false alarms, lead time, and compute.
Qualification
Robustness, explainability, and uncertainty.
Evidence result
Limitations and human authority remain attached.
P-101 example
| Field | Configuration | | ------------------------ | -------------------------------------------------------------- | | Asset | Boiler Feed Water Pump P-101 | | Objective | Detect bearing degradation | | Prediction horizon | 7 days | | Candidate models | Isolation Forest, XGBoost, Autoencoder, Physics Residual Model | | Candidate feature groups | temperature, vibration, process, physics-derived, temporal | | Validation | Walk-forward validation | | Operational constraint | Maximum one false alert per month |
The search target is the best solution for a particular machine, operating regime, failure mode, and operational constraint—not the algorithm with the highest accuracy. A detector with a high aggregate detection rate may be unusable if false alerts overload maintenance, if lead time disappears in a high-flow regime, or if its required sensors are unreliable.
Validation discipline
Time split, walk-forward, and leave-one-regime-out validation answer different questions. The split must respect temporal ordering and prevent future information from leaking into training. Metrics should be reported by operating regime and with uncertainty, not reduced to a single flattering average. Failed candidates remain part of the experiment history because they constrain later hypotheses and prevent expensive repetition.
Evidence Package
Every result binds the model, dataset, feature set, operating regimes, validation method, performance, false-alarm rate, lead time, robustness, uncertainty, explainability, limitations, data provenance, experiment ID, twin version, and code version. It is a model card for an engineering decision, not a deployment certificate.
If an industrial AI result cannot be reproduced, it should not be trusted.
The conceptual demonstrator compares deterministic fixtures so the interface and evidence contract can be inspected. It does not train models or claim measured performance. All seven metrics are authored teaching values, including resource timings; the fixed scoring adjustments cannot establish a superior algorithm. The hypothesis remains untested.
Use Export replay JSON to save the complete evidence package, configuration, seed, snapshot tick, and explicit behavior, experiment, world, simulator, metric, and schema versions. Import replay JSON checks the full record against the supported deterministic fixture before restoring it. Missing evidence, altered results, unknown versions, and unsupported configurations are rejected. This verifies fixture consistency only; it is not a signature, independent validation, or proof of industrial fitness.
Conceptual demonstration — synthetic fixture results.
P-101 bearing-degradation Evidence Package
EXP-P101-BD-COMBINED-XGBOOST-WALKFORWARD
Conceptual demonstration — synthetic fixture results.
Hypothesis — not tested
Bearing-related features may reveal degradation before failure under held-out operating regimes.
- All seven metrics are authored teaching values, not measurements, fitted models, or benchmark results.
- Feature and algorithm adjustments prescribe the displayed scores; they cannot establish a winning model or feature set.
- Validation selections label illustrative scenarios; no dataset is split and no statistical validation is executed.
- The fixed seed 101 identifies this fixture family; no random sampling occurs. Tick 0 is a single snapshot, not elapsed plant time.
- Sensor counts and inference milliseconds are illustrative resource assumptions, not an executed feature pipeline or hardware timing.
Replay identity
- Behavior version
- BEHAVIOR-P101-0.2.0
- Experiment version
- EXPERIMENT-P101-0.2.0
- World version
- WORLD-P101-0.2.0
- Metric version
- METRICS-P101-0.1.0
- Snapshot tick
- 0 / fixture snapshot
Experiment record
- Model
- MODEL-XGB-0.1.0
- Model status
- experimental
- Dataset
- DATASET-P101-SYN-0.2.0
- Feature set
- combined
- Validation
- walk-forward
- Twin version
- TWIN-P101-0.2.0
- Asset version
- ASSET-P101-0.2.0
- Dataset version
- DATASET-P101-SYN-0.2.0
- Simulator version
- SIM-P101-0.2.0
- Feature pipeline
- FEATURES-P101-0.2.0
- Provenance model
- MODEL-XGB-0.1.0
- Code version
- ITL-PHASE-1-0.2.0
- 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
| Evidence | Summary | Strength | Origin |
|---|---|---|---|
| EXP-P101-BD-COMBINED-XGBOOST-WALKFORWARD-SYNTHETIC-EVIDENCE | Deterministic synthetic fixture for conceptual comparison. | limited | Synthetic fixture |
Metric results
| Metric | Result |
|---|---|
| Detection Rate | 86 % |
| False Alarms | 0.9 alerts/month |
| Lead Time | 5 days |
| Inference Cost | 18 ms |
| Sensor Count | 11 sensors |
| Robustness | 80/100 |
| Explainability | 78/100 |
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
Four separate authorities
- Train a model
- Not performed. A future training run requires its own authorization.
- Recommend an action
- No operational recommendation is issued by this fixture.
- Approve an inference model
- Requires separate engineering review for a named model version and use.
- Change a physical setpoint
- Outside Phase 1. An inference approval never grants this authority.