Brito, SergioAzinheira, GonçaloSemião, JorgeSousa, NelsonPérez Litrán, Salvador2025-10-032025-10-032025-07-212079-9292http://hdl.handle.net/10400.1/27786Industrial 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.engPredictive maintenanceIoTVibration analysisWireless sensor networksMachine learningLow-cost sensorsFFTAnomaly detectionjournal article10.3390/electronics14142913