Building of transformer-based RUL predictors supported by explainability techniques: Application on real industrial datasets

• Hybrid deep learning architectures benchmark for RUL. • RUL applied on synthetics and real datasets. • Use of local XAI techniques to achieve trustworthy, transparent, and usable models. • Code and datasets of these experiments public available to contribute to future research. 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.