Predictive Maintenance in the Industry: A Comparative Study on Deep Learning-based Remaining Useful Life Estimation

Predictive Maintenance (PdM) aims to detect forth-coming failures in machinery to reduce costs associated with defective products and equipment inactivity. Remaining Useful Life (RUL) estimation is the most common approach in PdM: in this formalization, forecast or regression models aim at predicting the time/process iterations left before machinery loses its operation ability or a failure happens. In the RUL literature, Deep Learning (DL) algorithms are typically the preferred choice because they achieve high performance and can automatically handle the feature extraction phase. Usually, developed DL architectures are application or equipment specific; thus, there is no clear way to select, design, or implement such architectures. However, the research usually does not justify the choice of one architecture over another that may potentially work for the same problem. In addition, many of the reviewed papers do not investigate the computational complexity of these techniques, which is a critical aspect of real-time applications. In this work, we compare the most widely used deep learning architectures for performing RUL estimation in four datasets: two public datasets known in the PdM research community and two confidential industrial datasets. Moreover, we release a library called CeRULEo, to support the research within this field, speeding up the development of RUL models and providing a complete pre-processing pipeline for dataset handling.