Lighting maintenance management through IoT-based automated fault diagnostics

Monitoring building assets is fundamental for preventing failures during building utilization. This practice is performed in predictive maintenance activities, which are based on asset monitoring using sensors to predict and avoid failures. Even if it has been demonstrated how asset monitoring can save costs, time and reduce breakdowns, it is still rarely used in common practice by facility managers. This paper proposes a framework for monitoring lighting systems to predict failures and intervene before their occurrence. The monitoring system is composed of sensors that capture temperature, voltage, electrical power and current data from lighting systems. The system can compare the data gathered with the expected value and, if anomalies are detected, it sends a maintenance request or, in case of an emergency, can actuate the shutdown of the device and schedule emergency maintenance. Furthermore, a fault detection diagram is illustrated based on the more common failures detected in lighting systems. The proposed framework provides the theoretical foundation for the development of a system for laboratory experiments