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.

The overall objective of this thesis is to develop methods for fault detection and diagnosis for ground source heat pumps that can be used by servicemen to assist them to accurately detect and diagnose faults during the operation of the heat pump. The aim of this thesis is focused to develop two fault detection and diagnosis methods, sensitivity ratio and data-driven using principle component analysis. For the sensitivity ratio method model, two semi-empirical models for heat pump unit were built to simulate fault free and faulty conditions in the heat pump. Both models have been cross-validated by fault free experimental data. The fault free model is used as a reference. Then, fault trend analysis is performed in order to select a pair of uniquely sensitive and insensitive parameters to calculate the sensitivity ratio for each fault. When a sensitivity ratio value for a certain fault drops below a predefined value, that fault is diagnosed and an alarm message with that fault appears. The simulated faults data is used to test the model and the model successfully detected and diagnosed the faults types that were tested for different operation conditions. In the second method, principle component analysis is used to drive linear correlations of the original variables and calculate the principle components to reduce the dimensionality of the system. Then simple clustering technique is used for operation conditions classification and fault detection and diagnosis process. Each fault is represented by four clusters connected with three lines where each cluster represents different fault intensity level. The fault detection is performed by measuring the shortest orthogonal distance between the test point and the lines connecting the faults’ clusters. Simulated fault free and faulty data are used to train the model. Then, a new set of simulated faults data is used to test the model and the model successfully detected and diagnosed all faults type and intensity level of the tested faults for different operation conditions. Both models used simple seven temperature measurements, two pressure measurements (from which the condensation and evaporation temperatures are calculated) and the electrical power, as an input to the fault detection and diagnosis model. This is to reduce the cost and make it more convenient to implement. Finally, for each models, a user friendly graphical user interface is built to facilitate the model operation by the serviceman.