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

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Élément Dublin Core	Valeur	Langue
dc.contributor.author	Laid Abderrahmane	-
dc.contributor.author	Ouffa Wissal	-
dc.date.accessioned	2025-10-28T11:40:51Z	-
dc.date.available	2025-10-28T11:40:51Z	-
dc.date.issued	2025	-
dc.identifier.uri	https://di.univ-blida.dz/jspui/handle/123456789/40815	-
dc.description	4.621.1.1382;157p	fr_FR
dc.description.abstract	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.	fr_FR
dc.language.iso	en	fr_FR
dc.publisher	blida1	fr_FR
dc.subject	Edge-computing, STM32, NanoEdge, DMA, PCB	fr_FR
dc.title	Design and Implementation of a custom PCBBased Edge AI System Using STM32 for Real-Time State Detection and Condition Monitoring	fr_FR
Collection(s) :	Mémoires de Master

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