EV
EVOCON SolutionsIndustrial Predictive Quality
Plant 01 · Line 02 · Algorithm walkthrough · synthetic demonstration data
Local AI
System Online
Visual + sensor loop

From raw data to a prioritized action

The demo loops through collection, normality learning, deviation detection, risk scoring and verified improvement.

Part image
Camera + sensor input
Input
Heatmap + risk
Review output
Review
How the algorithm moves

Six decisions, one operator queue

Each step activates in sequence so the team can see what the product is doing.

Feature cloud

Normal points stay clustered; drifting observations move toward review.

1
Collect dataImages, sensors and process values.
IN
2
Learn normalNormal patterns become references.
OK
3
Spot deviationSmall visual or temporal changes appear.
!
4
Score riskPart, cycle and machine are ranked.
82
5
Recommend actionInspect first and confirm the cause.
FIX
Risk signal

Risk rises, gets prioritized, then returns under control

Risk is shown as an operating signal the team can act on.

0.28
Risk level rises as the point cloud and heatmap drift away from normal.
Normal
Monitor
Review
Risk

Recommended action

Inspect Part A-1842 first. Check tool wear and surface mark before scaling inspection to the next batch.

After action

Risk trend returns to monitor/normal. The system keeps watching for repeated deviation.

Machine / piece improvement

M02
Live

Risk rises when the process starts drifting, then drops after the recommended inspection action.

Deviation timeline

Temporal deviation starts before the final quality or maintenance problem becomes expensive.

What the operator sees

1Review top-ranked part or machine first.
2See where the anomaly appears and when it started.
3Confirm the fix and keep monitoring the next cycles.