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

비선형회귀모델을 이용한 히트펌프시스템의 열교환기 고장에 대한 고장감지 및 진단에 대한 연구

  • 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.)
  • 김학수 (서울대학교 기계항공공학부) ;
  • 김민수 (서울대학교 기계항공공학부)
  • Received : 2010.12.22
  • Accepted : 2011.09.21
  • Published : 2011.11.01

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.

본 연구에서는 비선형회귀모델을 이용한 히트펌프시스템에서의 고장감지 및 진단 알고리즘을 개발하였다. 히트펌프시스템에 발생할 수 있는 다양한 고장요소 중, 열교환기 고장에 대한 연구를 수행하였다. 해석 식을 바탕으로 제작한 모델을 이용하여 총 4가지 작동 모드(무고장, 증발기 고장, 응축기 고장, 응축기와 증발기 고장)에 대한 시뮬레이션을 수행하였다. 고장감지 및 진단 알고리즘을 개발하기 위해 무고장모드에서의 데이터를 바탕으로 각 열교환기의 과열도 또는 과냉도를 예측할 수 있는 비선형회귀모델을 제시하였다. 고장감지 및 진단 알고리즘은 이 비선형회귀모델을 바탕으로 예측한 열교환기에서의 과열도 또는 과냉도 값과 시뮬레이션 값을 비교하여 그 차이의 정도에 따라 각 열교환기의 고장을 감지 및 진단하도록 하였다.

Keywords

References

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