A low-cost data acquisition system for the optimization of maintenance strategies and fault diagnostics in heat pumps
Building sector comprises around 30% of the final energy consumption (FEC) worldwide whose forecasts indicate an increase of more than 50% by 2050. Therefore, it reveals an extensive potential for improvements, especially in the domain of Heating, Ventilation, and Air-Conditioning (HVAC), which accounts for about 50% of the FEC in buildings. In the HVAC framework, because of its great capability in providing heating, cooling, and domestic hot water efficiently, the heat pumps (HPs) market has been under a continuous expansion. Nevertheless, HPs clean operation might be jeopardised by the occurrence of faults. Accordingly, efficient methods for the fault detection and diagnostics (FDD) in HPs must be executed effectively. Although there are several FDD methods nowadays, including data-driven models, regardless of the method employed, equipment information is required either to conduct a punctual non-invasive FDD or to establish an oncondition based maintenance strategy. Since mostly FDD methods rely on the analysis of electromechanical parameters, such data acquisition might be costly. This paper addresses the conceptualization and prototyping of a low-cost data acquisition system for HPs based on a printed circuit board (PCB) with a microcontroller and ten temperature sensors to measure some the most important circuit parameters. The developed system might be used by either technicians in the field or embedded in the equipment itself for constant monitoring through Internet of Things (IoT) applications. Therefore, through the utilization of the measured parameters, it is possible to inspect the system and detect faults within it before they propagate or increase their severity. The collected data might be recorded and accessed physically, through a micro-Secure Digital (micro-SD) card, or remotely, through Bluetooth or Wi- Fi connection. The former improves the data to be analysed later for FDD, and the latter provides the possibility to implement an on-condition based maintenance strategy through an IoT approach.