Browse > Article
http://dx.doi.org/10.7734/COSEIK.2022.35.6.357

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

Jae-Hee, Kim (School of Mechanical Engineering, Pusan Nat'l University)
Yoojeong, Noh (School of Mechanical Engineering, Pusan Nat'l University)
Jong-Hwan, Jeung (SAC Research/Engineering Division, LG Electronics)
Bong-Soo, Choi (SAC Research/Engineering Division, LG Electronics)
Seok-Hoon, Jang (SAC Research/Engineering Division, LG Electronics)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.35, no.6, 2022 , pp. 357-365 More about this Journal
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;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Dunn, J. C. (1973) A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, J. Cybern., 3(3), pp.32~57.   DOI
2 Cao, S., Rhinehart, R.R. (1995) An Efficient Method for On-line Identification of Steady State, J. Process Control, 5(6), pp. 363~374.   DOI
3 Cortes, C., Vapnik, V. (1995) Support-vector Networks, Mach. Learn., 20(3), pp.273~297.
4 Kim, M., Yoon, S.H., Domanski, P.A., Payne, W.V. (2008) Design of a Steady-state Detector for Fault Detection and Diagnosis of a Residential Air Conditioner, Int. J. Refrig., 31(5), pp.790~799.   DOI
5 Kim, S., Kang, Y.-J., Noh, Y., Park, S., Ahn, B. (2021) Fault Classification Model based on Time Domain Feature Extraction of Vibration Data, J. Comput. Struct. Eng. Inst. Korea, 34(1), pp.25~33.   DOI
6 Kim, S., Noh, Y., Kang, Y.-J., Park, S., Lee, J.W., Chin, S.W. (2022) Hybrid Data-scaling Method for Fault Classification of Compressors, Meas., 201, p.111619.
7 Kim, W., Braun, J.E. (2012) Evaluation of the Impacts of Refrigerant Charge on Air Conditioner and Heat Pump Performance, Int. J. Refrig., 35, pp.1805~1814.   DOI
8 Li, Z., Shi, S., Chen, H., Wei, W., Wang, Y., Liu, Q., Liu, T. (2020) Machine Learning based Diagnosis Strategy for Refrigerant Charge Amount Malfunction of Variable Refrigerant Flow System, Int. J. Refrig., 110, pp.95~105.   DOI
9 Liu, A.J., Mukherjee, A., Hu, L., Chen, J., Nair, V.N. (2022) Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study, arXiv preprint arXiv :2204.12868.
10 Peng, H., Long, F., Ding, C. (2005) Feature Selection based on Mutual Information Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy, IEEE Trans. Pattern Anal. & Mach. Intell., 27, pp.1226~1238.   DOI
11 Sun, K., Li, G., Chen, H., Liu, J., Li, J., Hu, W. (2016) A novel Efficient SVM-based Fault Diagnosis Method for Multi-Split Air Conditioning System's Refrigerant Charge Fault Amount, Appl. Therm. Eng., 108, pp.989~998.   DOI
12 Wan, H., Cao, T., Hwang, Y., Chang, S.D., Yoon, Y.J. (2021) Machine-Learning-based Compressor Models: A Case Study for Variable Refrigerant Flow Systems, Int. J. Refrig., 123, pp.23~33.   DOI
13 Webb, A.R. (2003) Statistical Pattern Recognition. John Wiley & Sons.
14 Xu, D., Tian, Y. (2015) A Comprehensive Survey of Clustering Algorithms, Ann. Data Sci., 2, pp.165~193.   DOI
15 Yoo, J.W., Hong, S.B., Kim, M.S. (2017) Refrigerant Leakage Detection in an EEV Installed Residential Air Conditioner with Limited Sensor Installations, Int. J. Refrig., 78, pp. 157~165.   DOI