Development of a reference model to predict the value of system parameters during fault free operation is a basic step for fault detection and diagnosis (FDD). In order to develop an accurate and effective reference model of a heat pump system, experimental data that cover a wide range of operating conditions are required. In this study, laboratory data were collected under various operating conditions and then filtered through a moving window steady-state detector. Over sixteen thousand scans of steady-state data were used to develop polynomial regression models of seven system features using three independent variables. The reference model was also developed using an artificial neural network (ANN), and its advantages and disadvantages are discussed. The models were evaluated, and the root mean square error for each model was calculated and compared.
Citation: International Journal of Mechanical Sciences
Pub Type: Journals
artificial neutral network, heat pump fault detection, polynomial reference model