{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:05:53Z","timestamp":1773795953697,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T00:00:00Z","timestamp":1753056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agricultura Sostenible de C\u00edtricos con Inteligencia Artificial","award":["0085_ATTENTIA_5_E"],"award-info":[{"award-number":["0085_ATTENTIA_5_E"]}]},{"name":"Programa de Cooperaci\u00f3n Interreg Espa\u00f1a-Portugal (POCTEP) 2021\u20132027","award":["0085_ATTENTIA_5_E"],"award-info":[{"award-number":["0085_ATTENTIA_5_E"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Electronics"],"abstract":"<jats:p>Industrial maintenance has shifted from reactive repairs and calendar-based servicing toward data-driven predictive strategies. This paper presents a non-intrusive, low-cost IoT hardware platform for sustainable predictive maintenance of rotating machinery. The system integrates an ESP32-S3 sensor node that captures vibration (100 kHz) and temperature data, performs local logging, and communicates wirelessly. An automated spectral band segmentation framework is introduced, comparing equal-energy, linear-width, nonlinear, clustering, and peak\u2013valley partitioning methods, followed by a weighted feature scheme that emphasizes high-value bands. Three unsupervised one-class classifiers\u2014transformer autoencoders, GANomaly, and Isolation Forest\u2014are evaluated on these weighted spectral features. Experiments conducted on a custom pump test bench with controlled anomaly severities demonstrate strong anomaly classification performance across multiple configurations, supported by detailed threshold-characterization metrics. Among 150 model\u2013segmentation configurations, 25 achieved perfect classification (100% precision, recall, and F1 score) with ROC-AUC = 1.0, 43 configurations achieved \u226590% accuracy, and the lowest-performing setup maintained 81.8% accuracy. The proposed end-to-end solution reduces the downtime, lowers maintenance costs, and extends the asset life, offering a scalable, predictive maintenance approach for diverse industrial settings.<\/jats:p>","DOI":"10.3390\/electronics14142913","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T13:59:11Z","timestamp":1753106351000},"page":"2913","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Non-Intrusive Low-Cost IoT-Based Hardware System for Sustainable Predictive Maintenance of Industrial Pump Systems"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7287-0431","authenticated-orcid":false,"given":"S\u00e9rgio Duarte","family":"Brito","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Instituto Superior de Engenharia, University of Algarve, 8005-139 Faro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3139-2081","authenticated-orcid":false,"given":"Gon\u00e7alo Jos\u00e9","family":"Azinheira","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Instituto Superior de Engenharia, University of Algarve, 8005-139 Faro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7667-7910","authenticated-orcid":false,"given":"Jorge Filipe","family":"Semi\u00e3o","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Instituto Superior de Engenharia, University of Algarve, 8005-139 Faro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5205-8608","authenticated-orcid":false,"given":"Nelson Manuel","family":"Sousa","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Instituto Superior de Engenharia, University of Algarve, 8005-139 Faro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7002-4927","authenticated-orcid":false,"given":"Salvador P\u00e9rez","family":"Litr\u00e1n","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda El\u00e9ctrica y T\u00e9rmica, de Dise\u00f1o y Proyectos, Escuela T\u00e9cnica Superior de Ingenier\u00eda, Universidad de Huelva, 21007 Huelva, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the support of a high-dimensional distribution","volume":"13","author":"Platt","year":"2001","journal-title":"Neural Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.sigpro.2013.12.026","article-title":"A review of novelty detection","volume":"99","author":"Pimentel","year":"2014","journal-title":"Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1186\/s40537-021-00514-x","article-title":"A literature review on one-class classification and its potential applications in big data","volume":"8","author":"Seliya","year":"2021","journal-title":"J. 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