Live on ESP32 — try it now

Predict machine failures before they happen.

Watch an AI model detect 4 types of motor faults in real-time on an ESP32 chip. No cloud. No internet. Just 97 KB of pure C running on an 8 EUR microcontroller.

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Train a model in 30 seconds.
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Luviner Edge V3 — Interactive Training
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Training Results
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Luviner Predictive Maintenance — Live Simulation
ESP32 — 240 MHz — 512 KB RAM
100%
Fault detection accuracy
97 KB
Model size in flash
< 1 ms
Inference time
€8
Total hardware cost
Fault Detection
Four faults. One chip.
The model classifies vibration patterns from 8 accelerometers into 4 motor health states, in real-time.

Normal

Regular vibration pattern. Motor operating within specifications.

Rotor Imbalance

Strong 1x frequency component. Schedule balancing within 2 weeks.

Bearing Wear

High-frequency impulses detected. Replace bearing before failure.

Misalignment

Strong 2x and 3x harmonics. Realign shaft coupling.

The Problem
Unplanned downtime kills profits.
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Unexpected Failures

70% of motor failures come from bearings and misalignment — detectable weeks before breakdown.

10-100K EUR/hour
🕑

Traditional Approach

Hiring ML + embedded engineers to build a custom solution takes months and significant budget.

6-9 months
💰

Enterprise Platforms

Solutions like Edge Impulse or STM32Cube.AI require expensive licenses and lock you into their ecosystem.

20K+ EUR/year
Comparison
How Luviner compares.
The same result at a fraction of the cost and time.
Luviner Consultants Enterprise AI
Upfront cost from 2,400 EUR/yr 80-150K EUR 20K+ EUR/year
Time to deploy 1 week 6-9 months 2-3 months
Per-device cost 0.50 EUR Custom dev Enterprise license
Vendor lock-in No No Yes
Works offline Yes Depends Depends

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