ClarusC64/aviation-avionics-redundant-narrative-baseline-construction-v0.1

Dataset purpose

Construct the baseline of a healthy avionics narrative.

Modern aircraft contain multiple redundant computers and sensors. Each subsystem produces a coherent “story” about aircraft state. Under healthy conditions these stories align tightly.

This dataset defines that baseline alignment.

It captures:

• —expected agreement patterns between redundant units
• —allowable divergence bands
• —narrative coherence ranges during normal flight

The goal is not to detect failure yet. The goal is to define normal.

Once baseline coherence is known drift and divergence can be detected early.

What the model must learn

Given redundant subsystem outputs estimate whether the system narrative is still inside baseline bounds.

The model must identify:

• —baseline narrative coherence score
• —acceptable divergence range
• —early drift signals
• —systems still within tolerance

This dataset trains the first layer of the avionics narrative stack: baseline construction.

Downstream datasets handle:

• —drift detection
• —containment
• —reset strategy

Required outputs

• —baselinecoherencescore
• —divergencebandwidth
• —narrativealignmentstatus
• —earlydriftindicator
• —systemswithintolerance
• —notesonvariance

Why this matters

Hard failures are rare. Narrative drift comes first.

Redundant systems often remain internally consistent while slowly diverging from each other.

If baseline coherence is known these drifts can be caught before alarms trigger.

Use cases

• —early fault detection
• —redundancy health monitoring
• —cyber-physical anomaly detection
• —avionics integrity scoring
• —predictive maintenance