{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:55:45Z","timestamp":1778082945600,"version":"3.51.4"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686196","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,16]]},"abstract":"<jats:p>Predictive maintenance (PdM) in Industry 4.0 (I4.0) increasingly relies on machine learning (ML) techniques to minimize unplanned downtime and enhance operational efficiency. While cloud-based ML solutions offer scalability and strong predictive performance, their reliance on network connectivity introduces latency and reliability issues that hinder real-time industrial applications. This study investigates the deployment of lightweight autoencoder (AE)-based models optimized for edge computing environments, comparing their performance against traditional cloud-hosted alternatives. Multiple model architectures were evaluated, and inference latency was benchmarked across four deployment scenarios: cloud-hosted PyTorch, native PyTorch on Raspberry Pi 3B, TensorFlow Lite (Python runtime), and TensorFlow Lite (C++ runtime). Latency measurements, averaged 100 executions per model, reveal that edge deployment can reduce inference time by up to 7000\u00d7 compared to cloud containers, with TensorFlow Lite C++ deployments achieving latencies as low as 60 microseconds. These results demonstrate that edge-based ML deployment is a viable strategy for enabling timely, autonomous fault detection in real-time PdM systems.<\/jats:p>","DOI":"10.3233\/faia250534","type":"book-chapter","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:19:41Z","timestamp":1758028781000},"source":"Crossref","is-referenced-by-count":1,"title":["Edge-Enabled Predictive Maintenance with Autoencoders: A Real-Time Approach"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1668-9030","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Martins","sequence":"first","affiliation":[{"name":"University of Minho, ALGORITMI \u2013 LASI"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3608-0391","authenticated-orcid":false,"given":"Manuel","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"University of Minho, ALGORITMI \u2013 LASI"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3549-0754","authenticated-orcid":false,"given":"Paulo","family":"Novais","sequence":"additional","affiliation":[{"name":"University of Minho, ALGORITMI \u2013 LASI"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","New Trends in Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250534","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:19:41Z","timestamp":1758028781000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250534"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"ISBN":["9781643686196"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250534","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,16]]}}}