Version 1
: Received: 22 May 2023 / Approved: 23 May 2023 / Online: 23 May 2023 (04:28:23 CEST)
How to cite:
Laydner de Melo Rosa, G.; Mohanram, P.; Gilerson, A.; Schmitt, R. H. Architecture for Edge-Based Predictive Maintenance of Machines Using Federated Learning and Multi Sensor Platforms. Preprints2023, 2023051563. https://doi.org/10.20944/preprints202305.1563.v1
Laydner de Melo Rosa, G.; Mohanram, P.; Gilerson, A.; Schmitt, R. H. Architecture for Edge-Based Predictive Maintenance of Machines Using Federated Learning and Multi Sensor Platforms. Preprints 2023, 2023051563. https://doi.org/10.20944/preprints202305.1563.v1
Laydner de Melo Rosa, G.; Mohanram, P.; Gilerson, A.; Schmitt, R. H. Architecture for Edge-Based Predictive Maintenance of Machines Using Federated Learning and Multi Sensor Platforms. Preprints2023, 2023051563. https://doi.org/10.20944/preprints202305.1563.v1
APA Style
Laydner de Melo Rosa, G., Mohanram, P., Gilerson, A., & Schmitt, R. H. (2023). Architecture for Edge-Based Predictive Maintenance of Machines Using Federated Learning and Multi Sensor Platforms. Preprints. https://doi.org/10.20944/preprints202305.1563.v1
Chicago/Turabian Style
Laydner de Melo Rosa, G., Andre Gilerson and Robert Heinrich Schmitt. 2023 "Architecture for Edge-Based Predictive Maintenance of Machines Using Federated Learning and Multi Sensor Platforms" Preprints. https://doi.org/10.20944/preprints202305.1563.v1
Abstract
Artificial Intelligence (AI) models are expected to have a great impact in the manufacturing industry, optimizing time and resource cost by enabling applications such as predictive maintenance (PM) of production machines. A necessary condition for this is the availability of high quality data collected as close as possible to the process in question. With the advent of 5G equipped multi sensor platforms (MSPs), high sampling rate data can be collected and transmitted for processing in real time. This poses a data security challenge, since this data may give valuable insight into confidential business information of companies. Federated learning (FL) enables the training of AI models with data from multiple sources without it leaving the shop floor, by utilizing distributed computing resources available on premise. This paper introduces an architecture of FL based on data collected from 5G MSPs for enabling PM in industrial environments and discusses its potential benefits and challenges.
Keywords
artificial intelligence, federated learning, predictive maintenance, 5G, smart sensors, manufacturing, data security, data silos
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.