The maintenance of the Florence Tramway System is a challenging task. The infrastructure is stressed with trams rolling for around 7000 km everyday along the 3 existing lines. In this scenario the “preventive” approach, currently adopted in maintenance, is based mostly on industry averages and best practices, where different kind of triggers determine when to do service to the equipment; these triggers can be specific time interval expiration, defined traveled distance thresholds or the occurrence of specific conditions.
A step forward can be achieved by the transition from a “preventive” to a “predictive” maintenance approach where processes are refined and intervention frequencies are tuned on the basis of indicators “felt” from the system. This approach implies a revamping of the current equipment with a higher level of technology, such as the installation of IoT sensors and other software and hardware tools needed to collect information considered representative of the true and most updated picture of the system utilisation.