Prediction of maintenance equipment failures using automated machine learning - Ferreira, Luís (2021)
Predictive maintenance is a key area that is benefiting from the Industry 4.0 advent. Recently, there have been several attempts to use Machine Learning (ML) in order to optimize the maintenance of equipments and their repairs, with most of these approaches assuming an expert-based ML modeling. In this paper, we explore an Automated Machine Learning (AutoML) approach to address a predictive maintenance task related to a Portuguese software company. Using recently collected data from one of the company clients, we firstly performed a benchmark comparison study that included four open-source modern AutoML technologies to predict the number of days until the next failure of an equipment and also determine if the equipments will fail in a fixed amount of days. Overall, the results were very close among all AutoML tools, with AutoGluon obtaining the best results for all ML tasks. Then, the best AutoML predictive results were compared with a manual ML modeling approach that used the same dataset. The results achieved by the AutoML approach outperformed the manual method, thus demonstrating the quality of the automated modeling for the predictive maintenance domain.