• Transactions of the Korean Society of Mechanical Engineers B
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• v.32 no.6
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• pp.454-462
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• 2008

Since operating conditions are significantly different for heating and cooling mode operations in a $CO_2$ heat pump system, it is difficult to optimize the performance of the $CO_2$ cycle. In addition, the performance of a $CO_2$ heat pump is very sensitive to outdoor temperature and gascooler pressure. In this study, the cooling and heating performances of a variable speed $CO_2$ heat pump with a twin-rotary compressor were measured and analyzed with the variations of EEV opening and compressor frequency. As a result, the cooling and heating COPs were 2.3 and 3.0, respectively, when the EEV opening was 22%. When the optimal EEV openings for heating and cooling were 28% and 16%, the cooling and heating COPs increased by 3.3% and 3.9%, respectively, over the COPs at the EEV opening of 22%. Beside, the heating performance was more sensitive to EEV opening than the cooling performance. As the compressor speed decreased by 5 Hz, the cooling COP increased by 2%, while the heating COP decreased by 8%.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.