Learn-normal inspection architecture

How IntelFactor learns normal, detects deviations, and compounds accuracy over time.

What this page covers

IntelFactor uses a learn-normal → detect-deviation approach for product inspection.

It is designed for edge-first continuity and evidence-backed outcomes.

Related reading:

• Core principles

• System overview

• Edge pipeline overview

• Evidence & retention policy

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1. Two inspection paradigms

Both paradigms can use learn-normal methods.

They differ in what they treat as the “unit of truth”.

Dimension	Process monitoring (line-level)	Product inspection (unit-level)
Primary signal	Rhythm, cycle timing, station behavior	Visual conformity of each unit
ROI modeling	Whole-scene or station-wide	Per-component ROI modeling
Operator workflow	Video review and triage	Structured fast review loop
What you optimize	Process reliability	Quality + yield
Offline continuity	Often unspecified	Required for continuity
Promotion path	Optional	Anomaly → defect class (when stable)

IntelFactor is intentionally product-level.\n\nIt treats each inspected unit as ground truth for traceability and yield.

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2. IntelFactor architecture: learn normal at the product layer

IntelFactor learns what “normal” looks like per station and SKU.

It detects deviations and stores evidence locally, even during outages.

2.1 Core edge pipeline

Design principles

• Edge-first critical path (no cloud dependency)

• Deterministic gating (thresholds + geometric constraints)

• Evidence-first outcomes (buffer locally, sync later)

• Bounded responsibility (IntelFactor reports outcomes; control stays external)

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3. The compounding learning loop

IntelFactor improves through structured review and promotion.

The goal is fewer false positives and more actionable defect classes over time.

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4. What improves over time (and why)

4.1 Product-level precision

Unit-level inspection enables:

• Component-level ROI segmentation

• Multi-camera fusion (front + side)

• Defect density metrics per batch

• First-pass yield tracking

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4.2 Deterministic decision layer

Model scores are not the final decision.

IntelFactor uses deterministic gates so outcomes stay predictable:

• Confidence thresholds per inspection rule

• Minimum anomaly area filters

• Temporal persistence requirements (optional)

• Outcome metadata tied to evidence

Determinism makes deployments easier to validate.\n\nIt also makes operations safer during change.

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4.3 Drift + stability scoring

Factories drift. Lighting changes. Materials change.

IntelFactor tracks stability signals so drift becomes visible:

• Embedding centroid drift

• Anomaly inflation rate

• Review queue volume spikes

• Lighting variance shifts

When stability drops, IntelFactor can guide recalibration:

• Capture a refreshed “normal” baseline

• Compare deltas against the prior baseline

• Require approval before production threshold changes

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4.4 Hybrid promotion strategy (anomaly → defect class)

Anomalies are discovery.

Defect classes are how you scale.

IntelFactor supports promotion when a pattern is stable:

• Cluster repeat anomalies

• Name the defect type

• Train a supervised detector when labels are mature

Typical outcomes:

• Fewer false positives over time

• Better structured analytics

• A growing defect taxonomy

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5. Integration outputs (production-friendly)

IntelFactor produces PASS/FAIL outcomes at the edge.

It can publish outcomes for integration, such as:

• API events to MES / QMS

• Discrete I/O or fieldbus signals to a PLC (site-owned logic)

• Evidence references for traceability

IntelFactor does not replace PLC logic or safety systems.\n\nSites remain responsible for actuation logic and safety validation.

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6. Risks & mitigations

Risk	Mitigation
False positives	Operator feedback loop + threshold tuning
Lighting drift	Stability scoring + guided recalibration
Product variants	Per-SKU baselines and ROI configs
Internet outage	Local buffering + offline continuity
Model regression	Evaluation gates before deploy
Operator friction	Fast review UX with minimal clicks

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7. Guardrails

• No “text trains vision” claims

• No cloud dependency in the inspection critical path

• No auto-deploy of new models without evaluation

• No outcomes without evidence (heatmaps + artifacts)

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8. Example rollout plan (30 / 60 / 90 days)

1

First 30 days

• Deploy an anomaly baseline on the edge

• Stand up deterministic gating

• Validate evidence capture + retention

• Start the operator review loop

2

Next 60 days

• Add stability scoring

• Enable anomaly clustering

• Add defect promotion workflow

• Add model evaluation gates

3

Next 90 days

• Add multi-camera fusion where needed

• Expand per-SKU baseline support

• Add yield analytics

• Harden offline resilience and rollback behavior

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9. Summary

IntelFactor applies learn-normal methods to unit-level inspection.

It pairs those methods with deterministic gates and evidence.

It is designed for real constraints: latency, outages, and drift.