Refrigerant charge fault diagnosis in the VRF system using Bayesian artificial neural network combined with ReliefF filter

Shubiao Shi, Guannan Li*, Huanxin Chen, Jiangyan Liu, Yunpeng Hu, Lu Xing, Wenju Hu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

A proper refrigerant charge amount (RCA) is critical for a variable refrigerant flow (VRF) system since RCA may affect the operational performance. However, there were few studies of RCA fault for the VRF system in the open literature. Therefore VRF systems are calling for a fault diagnosis strategy. This paper develops a highly efficient fault diagnosis model (FDM), which employs the ReliefF algorithm for feature ranking (FR) and applies the neural network for fault diagnosis. Firstly, the artificial neural network (ANN) model is built on the N-best features data subset and optimized by the Bayesian regularization algorithm. Secondly, the model is verified by testing data subset, the correct diagnosis rates (CDR) using the N-best features data subset can be obtained. The optimal FDM is selected in consideration of CDR and the computational efficiency. Finally, optimal FDM is further optimized by selecting the best hidden neurons. The results show that the CDR of the FDM based on 6-best features is sufficiently high in comparison to the CDR achieved when 22 features are used, while the training time decreases by 98.8%.

Original languageEnglish
Pages (from-to)698-706
Number of pages9
JournalApplied Thermal Engineering
Volume112
Early online date15 Oct 2016
DOIs
Publication statusPublished - 5 Feb 2017
Externally publishedYes

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