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An online compressor liquid floodback fault diagnosis method for variable refrigerant flow air conditioning system.

Author(s) : ZHOU Z., WANG J., WEI W., XU C.

Type of article: IJR article

Summary

Compressor liquid floodback not only can drop the performance of the variable refrigerant flow (VRF) system, but also may cause mechanical failure. Therefore, this is necessary to perform a timely online diagnosis of that fault. The present paper proposes an online compressor liquid floodback fault diagnosis method for VRF system based on back-propagation neural network (BPNN), which fills the online compressor liquid floodback fault diagnosis knowledge gap. The proposed method main article context as follow: Firstly, the sensors in VRF system record and save the data every three seconds to form the raw dataset. Secondly, after preprocessing the raw dataset, the correlation analysis is used to filter the data variables. Thirdly, the BPNN model is established by using back-propagation neural network algorithm. The testing dataset was used to verify the reliability of the model. The online dataset was used to test the model's online diagnostic capability, and its results were also compared and analyzed to the Classification and Regression Tree (CART) model. This result indicates that the BPNN method has a low degree of over-fitting and high reliability, and its online diagnostic accuracy is up to 99.48%. By comparing results of the method BPNN and CART, it shows that the former is superior to the latter, regardless of the diagnostic accuracy or the online diagnostic stability.

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Pages: 9-19

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Details

  • Original title: An online compressor liquid floodback fault diagnosis method for variable refrigerant flow air conditioning system.
  • Record ID : 30027362
  • Languages: English
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 111
  • Publication date: 2020/03
  • DOI: http://dx.doi.org/10.1016/j.ijrefrig.2019.11.024

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