Hybrid Long-Range–5G Multi-Sensor Platform for Predictive Maintenance for Ventilation Systems
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| Published in: | Electronics vol. 14, no. 5 (2025), p. 1055 |
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| Main Author: | |
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MDPI AG
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| Online Access: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3176377490 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14051055 |2 doi | |
| 035 | |a 3176377490 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Mohanram, Praveen |u Production Metrology, Fraunhofer Institute for Production Technology IPT, 52074 Aachen, Germany | |
| 245 | 1 | |a Hybrid Long-Range–5G Multi-Sensor Platform for Predictive Maintenance for Ventilation Systems | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Data analysis | ||
| 653 | |a Ventilation | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Sensors | ||
| 653 | |a Hardware | ||
| 653 | |a 5G mobile communication | ||
| 653 | |a Cloud computing | ||
| 653 | |a Maintenance management | ||
| 653 | |a Predictive control | ||
| 653 | |a Industrial Internet of Things | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Multisensor applications | ||
| 653 | |a Real time | ||
| 653 | |a Predictive maintenance | ||
| 700 | 1 | |a Schmitt, Robert H |u Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, 52062 Aachen, Germany; <email>robert.schmitt@wzl-iqs.rwth-aachen.de</email> | |
| 773 | 0 | |t Electronics |g vol. 14, no. 5 (2025), p. 1055 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3176377490/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3176377490/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3176377490/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |