RT Journal Article
T1 Non-Intrusive Low-Cost IoT-Based Hardware System for Sustainable Predictive Maintenance of Industrial Pump Systems
A1 Duarte Brito, Sérgio
A1 Azinheira, Gonçalo José
A1 Semião, Jorge
A1 Sousa, Nelson Manuel
A1 Pérez Litrán, Salvador
AB 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–valley partitioning methods, followed by a weighted feature scheme that emphasizes high-value bands. Three unsupervised one-class classifiers—transformer autoencoders, GANomaly, and Isolation Forest—are 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–segmentation configurations, 25 achieved perfect classification (100% precision, recall, and F1 score) with ROC-AUC = 1.0, 43 configurations achieved ≥90% 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.
PB MDPI
SN 2079-9292 (electrónico)
YR 2025
FD 2025
LK https://hdl.handle.net/10272/27477
UL https://hdl.handle.net/10272/27477
LA eng
NO Brito, S. D., Azinheira, G. J., Semião, J. F., Sousa, N. M., & Pérez Litrán, S. (2025). Non-Intrusive Low-Cost IoT-Based Hardware System for Sustainable Predictive Maintenance of Industrial Pump Systems. Electronics, 14(14), 2913. https://doi.org/10.3390/electronics14142913
NO This work was supported by research project Agricultura Sostenible de Cítricos con Inteligencia Artificial (0085_ATTENTIA_5_E), Programa de Cooperación Interreg España-Portugal (POCTEP) 2021–2027.
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026