Automated IoT-Connected On-Board Fault Detection in Fan-Coils: Prototype Construction and Preliminary

The paper focuses on design and implementation of an IoT connected on-board automated fault detection and diagnostics prototype (AFDD) for a nonspecific fan-coil, in turn part of HVAC system and of a distributed digital collaboration framework used in Facility Management. A research on common IoT architecture and maintenance strategies has been carried out besides the theoretical development of a Fault detection diagram on all the typical faults in fan-coil units. A real fan-coil was then inspected to point out its construction details and the points to be monitored. Then it was equipped with the prototype AFDD system. All the components and sensors needed to build the AFDD prototype are commonly available. The design and implementation of automated fault detection and diagnostics (AFDD) for HVAC fan-coils systems fully exploits distributed computing for remote and smart system monitoring, anomaly detection and eventually fault diagnostics to improve maintenance management through the integration of a large number of data locally gathered by smart sensors. Experimental results on the prototype are given about some recurrent fan-coil anomalies. Local intelligence allows a quick and on-site anomaly detection and fault diagnostic, as proven by running the prototype AFDD equipped fan-coil: it could help managing and scheduling maintenance, reducing time-to-fix together with indirect and direct costs, if network connected. Feeding the network with relevant data about the anomalies extracted by the local intelligence allows sharing the information at every level, also in order to statistically rate HAVC components service life and reliability.