Design and Implementation of a custom PCBBased Edge AI System Using STM32 for  Real-Time State Detection and Condition Monitoring

Veuillez utiliser cette adresse pour citer ce document : https://di.univ-blida.dz/jspui/handle/123456789/40815

Titre:	Design and Implementation of a custom PCBBased Edge AI System Using STM32 for Real-Time State Detection and Condition Monitoring
Auteur(s):	Laid Abderrahmane Ouffa Wissal
Mots-clés:	Edge-computing, STM32, NanoEdge, DMA, PCB
Date de publication:	2025
Editeur:	blida1
Résumé:	This thesis presents an edge-computing system for real-time industrial machine monitoring, combining an STM32F103RB microcontroller with NanoEdge AI Studio to Monitor Machine state on-device without cloud dependency. Vibration data from an MPU6050 sensor is captured via DMA, analyzed by embedded AI, and displayed locally on an OLED while being relayed to a web dashboard (via CAN bus and ESP32). A custom 6-layer PCB and modular firmware architecture ensure low-latency processing and scalability. The solution demonstrates how lightweight embedded AI can enable cost-effective predictive maintenance.
Description:	4.621.1.1382;157p
URI/URL:	https://di.univ-blida.dz/jspui/handle/123456789/40815
Collection(s) :	Mémoires de Master

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Fichier	Description	Taille	Format
Master_Thesis_ESE7 1382-9554.pdf		8,48 MB	Adobe PDF	Voir/Ouvrir

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