Developing Remote Diagnostics Platform for De-vice Vault : Building a Scalable Remote Diagnostics demo
Menendez, Aurelio (2025)
2025
All rights reserved. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202505028603
https://urn.fi/URN:NBN:fi:amk-202505028603
Tiivistelmä
This thesis focuses on the design and implementation of a proof of concept of a modular remote diagnostics system that complements proprietary industrial software. The solution was built on a micro-services architecture using Java Spring Boot, AMQP was used for asynchronous service communication.
In the developed system, real-time data is collected from sensor-equipped edge devices, that are connected via serial and TCP communication. Data is then analyzed within a centralized data-processing service. Device integration, fault simulation, and live heartbeat tracking are supported, allowing for dynamic device state management and notification system.
A Reactbased web dashboard provides real-time monitoring and control. The system enables secure, scalable, and maintainable diagnostics workflows across services such as monitoring, and notification handling. The proof-of-concept implementation demonstrates how standard open-source technologies can be designed to deliver reliable remote insights into operational equipment, making it a viable foundation for future development in industrial diagnostics.
In the developed system, real-time data is collected from sensor-equipped edge devices, that are connected via serial and TCP communication. Data is then analyzed within a centralized data-processing service. Device integration, fault simulation, and live heartbeat tracking are supported, allowing for dynamic device state management and notification system.
A Reactbased web dashboard provides real-time monitoring and control. The system enables secure, scalable, and maintainable diagnostics workflows across services such as monitoring, and notification handling. The proof-of-concept implementation demonstrates how standard open-source technologies can be designed to deliver reliable remote insights into operational equipment, making it a viable foundation for future development in industrial diagnostics.