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http://dx.doi.org/10.3795/KSME-B.2011.35.11.1111

Fault Detection and Diagnosis (FDD) Using Nonlinear Regression Models for Heat Exchanger Faults in Heat Pump System  

Kim, Hak-Soo (School of Mechanical and Aerospace Engineering, Seoul Na'l Univ.)
Kim, Min-Soo (School of Mechanical and Aerospace Engineering, Seoul Na'l Univ.)
Publication Information
Transactions of the Korean Society of Mechanical Engineers B / v.35, no.11, 2011 , pp. 1111-1117 More about this Journal
Abstract
This paper proposed a fault detection and diagnosis (FDD) algorithm using nonlinear regression models, focusing especially on heat exchanger faults. This research concerned four working modes: those with no fault, evaporator fault, condenser fault, and evaporator and condenser faults. This research used no fault mode data to create an FDD algorithm. Using the no fault mode data, correlation functions for predicting the degree of superheat or subcool of heat exchangers (an evaporator and a condenser) were derived. Each correlation function has five inputs and one output. Based on these correlation functions, it is possible to predict the degree of superheat or subcool of each heat exchanger under various working conditions. The FDD algorithm was developed by comparing the predicted value and the simulation value. The FDD algorithm works well in all four working modes.
Keywords
Heat Pump; Fault; Detection; Diagnosis; Heat Exchanger;
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Times Cited By KSCI : 1  (Citation Analysis)
Times Cited By SCOPUS : 0
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