A.E. Outlook, Energy information administration, Department of Energy, 92010(9) (2010) 1-15.
 N. Kocyigit, H. Bulgurcu, C.-X. Lin, Fault diagnosis of a vapor compression refrigeration system with hermetic reciprocating compressor based on ph diagram, International journal of refrigeration, 45 (2014) 44-54.
 M. Stylianou, D. Nikanpour, Performance monitoring, fault detection, and diagnosis of reciprocating chillers, 0001-2505, American Society of Heating, Refrigerating and Air-Conditioning Engineers …, 1996.
 T.M. Rossi, J.E. Braun, A statistical, rule-based fault detection and diagnostic method for vapor compression air conditioners, Hvac&R Research, 3(1) (1997) 19-37.
 J.E. Braun, Automated fault detection and diagnostics for vapor compression cooling equipment, Journal of solar energy engineering, 125(3) (2003) 266-274.
 H. Han, B. Gu, T. Wang, Z. Li, Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning,International Journal of Refrigeration, 34(2) (2011) 586- 599.
 A. Janecke, T.J. Terrill, B.P. Rasmussen, A comparison of static and dynamic fault detection techniques for transcritical refrigeration, International Journal of Refrigeration, 80 (2017) 212-224.
 J. Choi, Y. Kim, Influence of the expansion device on the performance of a heat pump using R407C under a range of charging conditions, International Journal of Refrigeration, 27(4) (2004) 378-384.
 R. Holder. Charging Methods for Metering Devices, ACHR News, 2000 October 16.
 J.A. Siegel, An evaluation of superheat-based refrigerant charge diagnostics for residential cooling systems, (2002).
 N.S. Castro, Performance evaluation of a reciprocating chiller using experimental data and model predictions for fault detection and diagnosis/Discussion, ASHRAE Transactions, 108 (2002) 889.
 S. Tassou, I. Grace, Fault diagnosis and refrigerant leak detection in vapour compression refrigeration systems, International Journal of Refrigeration, 28(5) (2005) 680- 688.
 Z. Wang, L. Wang, K. Liang, Y. Tan, Enhanced chiller fault detection using Bayesian network and principal component analysis, Applied Thermal Engineering, 141 (2018) 898-905.
 G. Bogdanovská, V. Molnár, G. Fedorko, Failure analysis of condensing units for refrigerators with refrigerant R134a, R404A, International Journal of Refrigeration, 100 (2019) 208-219.
 F. Yu, G. Li, H. Chen, Y. Guo, Y. Yuan, B. Coulton, A VRF charge fault diagnosis method based on expert modification C5. 0 decision tree, International Journal of Refrigeration, 92 (2018) 106-112.
 Q. Mao, X. Fang, Y. Hu, G. Li, Chiller sensor fault detection based on empirical mode decomposition threshold denoising and principal component analysis, Applied Thermal Engineering, 144 (2018) 21-30.
 J. Liu, G. Li, B. Liu, K. Li, H. Chen, Knowledge discovery of data-driven-based fault diagnostics for building energy systems: A case study of the building variable refrigerant flow system, Energy, 174 (2019) 873-885.
 Y. Wang, Z. Wang, S. He, Z. Wang, A practical chiller fault diagnosis method based on Discrete Bayesian Network, International Journal of Refrigeration, (2019).
 B. Chen, J. Braun, Simple fault detection and diagnosis methods for packaged air conditioners, (2000).
 A. Handbook, Refrigeration, American Society of Heating, Refrigeration and Air Conditioning, (2006).
 M. Kim, M.S. Kim, Performance investigation of a variable speed vapor compression system for fault detection and diagnosis, International Journal of Refrigeration, 28(4) (2005) 481-488.