Development of Classification Model on SAC Refrigerant Charge Level Using Clustering-based Steady-state Identification

Jae-Hee Kim; Yoojeong Noh; Jong-Hwan Jeung; Bong-Soo Choi; Seok-Hoon Jang

doi:10.7734/COSEIK.2022.35.6.357

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2022 Vol.35, Issue 6 Preview Page Next Page

Research Paper

Development of Classification Model on SAC Refrigerant Charge Level Using Clustering-based Steady-state Identification 군집화 기반 정상상태 식별을 활용한 시스템 에어컨의 냉매 충전량 분류 모델 개발

31 December 2022. pp. 357-365

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Abstract

Refrigerant mischarging is one of the most frequently occurring failure modes in air conditioners, and both undercharging and overcharging degrade cooling performance. Therefore, it is important to accurately determine the amount of charged refrigerant. In this study, a support vector machine (SVM) model was developed to multi-classify the refrigerant mischarge through steady-state identification via fuzzy clustering techniques. For steady-state identification, a fuzzy clustering algorithm was applied to the air conditioner operation data using the difference between moving averages. The identification results using the proposed method were compared with those using existing steady-state determination techniques studied through the inversed Fisher's discriminant ratio (IFDR). Subsequently, the main features were selected using minimum redundancy maximum relevance (mRMR) considering the correlation among candidate features, and an SVM multi-classification model was devised using the derived features. The proposed method achieves satisfactory accuracy and robustness from test data collected in the new domain.

Keywords

system air conditioner

refrigerant mischarging

steady-state filter

moving average difference

fuzzy C-means clustering

mRMR

냉매 오충전은 에어컨에서 빈번하게 발생하는 고장 모드 중 하나로, 적정 충전량 대비 부족 및 과충전 모두 냉방 성능의 저하를 유발하므로 충전된 냉매량을 정확하게 판단하는 것이 중요하다. 본 연구에서는 퍼지 군집화 기법을 통한 정상상태 식별을 통해 냉매 오충전량을 다중 분류하는 모델을 개발하였다. 정상상태 식별을 위해 에어컨 운전 데이터에 대해 이동 평균 간의 차이를 활용한 퍼지 군집화 알고리즘을 적용하였으며, IFDR를 통해 기존 연구된 정상상태 판단 기법들과 식별 결과를 비교하였다. 이후, 시스템 내 상관성을 고려한 mRMR을 이용해 특징을 선택하였으며, 도출된 특징을 이용해 SVM 기반의 다중 분류 모델이 생성되었다. 제안된 방법은 시험 데이터를 통해 만족할 만한 분류 정확도와 강건성을 도출하였다.

키워드

시스템에어컨

냉매 오충전

정상상태 필터

이동평균차

퍼지 C-평균 군집화

최소중복최대상관

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Information

Publisher :Computational Structural Engineering Institute of Korea
Publisher(Ko) :한국전산구조공학회
Journal Title :Journal of the Computational Structural Engineering Institute of Korea
Journal Title(Ko) :한국전산구조공학회 논문집
Volume : 35
No :6
Pages :357-365
Received Date : 2022-10-18
Revised Date : 2022-11-15
Accepted Date : 2022-11-15
DOI :https://doi.org/10.7734/COSEIK.2022.35.6.357

Journal of the Computational Structural Engineering Institute of Korea ISSN:1229-3059(Print) 2287-2302(Online) 한국전산구조공학회 논문집

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