| Summary: | This research project is part of a wider project called Smart Fault Detection and Diagnosis for HeatPump Systems currently under development by the Royal Institute of Technology (KTH).Generally, maintenance, diagnosis and repair of heat pumps are manual operations. The qualityof the service relies almost exclusively on the skills, experience and motivation of the HVAC-Rtechnician. Moreover, professional technicians are only called up after a remarkable failure occursand not to perform routine follow up.The main objective of this master thesis will be to propose a method for fault detection of thebrine to water heat pump systems under operating conditions. It will be done by focusing into ninetests faults related to the first boundary level which represents the heat pump unit, the brine andwater loop. A model based approach was developed to generate features and parameters capableof reading the status of the system. The fault detection was done by imposing test faults in the model and evaluating the trend of the performance parameters. By comparing the predicted fault free values with the actual values (Residuals) from the model, several algorithms were proposed and conducted in order to obtain an online fault detection and diagnosis. It is concluded that the fault trend analysis can, in principle, provide a solution to detect faults in heat pump systems. The algorithms are considered user friendly tools, however more improvementsneeds to be done to include more faults and increase its resolution.
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