Search for: All records

Faults in components (valves, sensors, etc.) of radiant floor heating and cooling systems affect the efficiency, cooling and heating capacity as well as the reliability of the system. While various fault detection and diagnostic (FDD) methods have been developed and tested for building systems, FDD algorithms for radiant heating and cooling systems have previously not been available. This paper presents an evolving learning-based FDD approach for a radiant floor heating and cooling system based on growing Gaussian mixture regression (GGMR). The experimental space was controlled with a building automation system (BAS) in which the operating conditions can be monitored, and control parameters can be overridden to desired values. Trend data for normal operation and faulty operation were collected. A total of six fault types with different severities in a radiant floor system were emulated through overriding control parameters. An FDD model based on the GGMR approach was developed with training data and the performance of the model was tested for "known" faults that were including in the training and new "unknown" faults that were implemented in the fault testing. The prediction accuracy for each known fault was extremely high with the lowest prediction accuracy of 98% for one of the faults. The algorithm was successful in detecting the new fault as an unknown state before evolving the model and in diagnosing it as a new fault after evolving the model.