A Machine Learning-based Approach for Advanced Monitoring of Automated Equipment for the Entertainment Industry

Machine Learning-based approaches are revolutionizing the way in which complex systems and machines are monitored and controlled. In this work, we present a smart monitoring system that combines a big data architecture with an unsupervised anomaly detection technique, targeting the automated equipment in the entertainment industry. Anomaly detection uses state-of-the-art univariate and multivariate algorithms, as well as recently proposed techniques in the field of explainable artificial intelligence, to achieve enhanced monitoring capabilities and optimize service operations. The monitoring system is here presented and tested on a real world case study, i.e., an amusement park ride.