AutoML for Advanced Monitoring in Digital Manufacturing and Industry 4.0

The emergence of Industry 4.0 and the associated rise of sensing technologies in industrial equipment has made the adoption of Machine Learning (ML) solutions crucial in enhancing and making more efficient enterprise production processes. However, the high demand for ML models often clashes with the small number of professionals capable of handling such projects. For this reason, Automatic Machine Learning (AutoML) tools are considered high-value solutions, thanks to their capability to provide a suitable model for the provided data without the need for the intervention by a ML expert. Indeed, AutoML libraries are designed to be used in an easy way also for people with limited or no experience with ML. In this work, three important tasks involved in manufacturing, that are Anomaly Detection, Visual Anomaly Detection, and Remaining Useful Life Estimation, are considered. After analysing the most critical aspects of each task and the state of the art, possible solutions are proposed for the development of specialised AutoML modules. Additionally, given the increasing emphasis on the interpretability of MLmodels, part of the analysis performed aims at identifying explainability tools, which are particularly important for an AutoML library. In fact, they provide useful motivations for the model predictions, increasing also the user confidence in AutoML tools.