Lourenço, AfonsoFerraz, CarolinaMeira, JorgeMarreiros, GoretiBolón-Canedo, VerónicaAlonso-Betanzos, Amparo2026-09-142026-09-142023A. Lourenço, C. Ferraz, J. Meira, G. Marreiros, V. Bolón Canedo, and A. Alonso-Betanzos, "Automated Green Machine Learning for Condition-based Maintenance", ESANN 2023 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2023, pp. 291-296. https://doi.org/10.14428/esann/2023.ES2023-85978-2-87587-088-9https://hdl.handle.net/2183/49219Presented at: 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2023), 4-6 October 2023, Bruges (Belgium). Available at: https://www.esann.org/proceedings/2023[Abstract]: Within the big data paradigm, there is an increasing demand for machine learning with automatic configuration of hyperparameters. Although several algorithms have been proposed for automatically learning time-changing concepts, they generally do not scale well to very large databases. In this context, this paper presents an automated green machine learning approach applied to condition-based maintenance with automatic data fusion and density-based anomaly detection based on locality sensitivity hashing. Experiments on numerical simulations of train-track dynamic interactions demonstrate the utility of the approach to detect railway wheel out-of-roundness. This unlocks the full potential of scalable machine learning, paving the way for environment-friendly systems and automated decision-making.eng© ESANN 2023. All rights reserved. This is the published version of the paper, distributed in accordance with ESANN's self-archiving policy, which allows authors to archive their work in any repository provided that full reference is made to the ESANN publication.Machine learningCondition-based maintenanceAnomaly detectionconference outputopen access10.14428/esann/2023.ES2023-85