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Hybrid Long-Range–5G Multi-Sensor Platform for Predictive Maintenance for Ventilation Systems

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Publicado no:Electronics vol. 14, no. 5 (2025), p. 1055
Autor principal: Mohanram, Praveen
Outros Autores: Schmitt, Robert H
Publicado em:
MDPI AG
Assuntos:
Data analysis
Ventilation
Artificial intelligence
Sensors
Hardware
5G mobile communication
Cloud computing
Maintenance management
Predictive control
Industrial Internet of Things
Energy efficiency
Multisensor applications
Real time
Predictive maintenance
Acesso em linha:Citation/Abstract
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Resumo:In this paper, we present a multi-sensor platform for predictive maintenance featuring hybrid long-range (LoRa) and 5G connectivity. This hybrid approach combines LoRa’s low-power transmission for energy efficiency with 5G’s real-time data capabilities. The hardware platform integrates multiple sensors to monitor machine health parameters, with data analyzed on the device using pre-trained AI models to assess the machine’s condition. Inferences are transmitted via LoRa to the operator for maintenance scheduling, while a cloud application tracks and stores sensor data. Periodic sensor data bursts are sent via 5G to update the AI model, which is then delivered back to the platform through over-the-air (OTA) updates. We provide a comprehensive overview of the hardware architecture, along with an in-depth analysis of the data generated by the sensors, and its processing methodology. However, the data analysis and the software for ventilation control and its predictive capabilities are not the focus of this paper and are not presented.
ISSN:2079-9292
DOI:10.3390/electronics14051055
Fonte:Advanced Technologies & Aerospace Database