@article{10902/38558,
year = {2026},
month = {3},
url = {https://hdl.handle.net/10902/38558},
abstract = {One of the key aspects of Industry 4.0 is using intelligent systems to optimize manufacturing processes by improving productivity and reducing costs. These systems have greatly impacted in different areas, such as demand prediction and quality assessment. However, the prognostics and health management of industrial equipment is one of the areas with greater potential. This paper presents a comparative analysis of deep learning architectures applied to the prediction of the remaining useful life (RUL) on public real industrial datasets. The analysis includes some of the most commonly employed recurrent neural network variations and a novel approach based on a hybrid architecture using transformers. Moreover, we apply explainability techniques to provide comprehensive insights into the model's decision-making process. The contributions of the work are: (1) a novel transformer-based architecture for RUL prediction that outperforms traditional recurrent neural networks; (2) a detailed description of the design strategies used to construct the models on two under-explored datasets; (3) the use of explainability techniques to understand the feature importance and to explain the model's prediction and (4) making models built for reproducibility available to other researchers.},
organization = {This article is co-funded by the Spanish Government and FEDER funds (AEI/FEDER, UE) under grant PID2021-124502OB-C42 (PRESECREL); the predoctoral program “Concepción Arenal del Programa de Personal Investigador en formación Predoctoral” funded by Universidad de Cantabria and Cantabria’s Government (BOC 18-10-2021); the European Regional Development Fund (ERDF) through the Innovation and Digital Transition Programme (COMPETE 2030) under Portugal 2030; and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia, I.P. (Portuguese Foundation for Science and Technology) within project F.},
publisher = {Elsevier},
publisher = {Information Fusion, 2026, 127(Part C) 103892},
title = {Building of transformer-based RUL predictors supported by explainability techniques: application on real industrial datasets},
author = {Dintén Herrero, Ricardo and Zorrilla Pantaleón, Marta E. and Veloso, Bruno and Gama, João},
}