An enhanced PCA method with Savitzky-Golay method for VRF system sensor fault detection and diagnosis

Yabin Guo, Guannan Li, Huanxin Chen*, Yunpeng Hu, Haorong Li, Lu Xing, Wenju Hu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

Sensor faults of air conditioning systems are harmful to optimal control strategies and system performance resulting in poor control of the indoor environment and waste of energy. In order to improve the fault detection and diagnosis (FDD) performance, this paper presents an enhanced sensor fault detection and diagnosis method based on the Satizky-Golay (SG) method and principal component analysis (PCA) method for the VRF system, namely SG-PCA method. Due to the volatility of the original data set of VRF system, the original data are smoothed using SG method at first. Then, the smoothed data are used for PCA model training and fault detection and diagnosis. In order to determine parameters of the SG method, an optimization index is proposed, which is calculated by the signal to noise ratio (SNR), the standard deviation (SD) and the self-detection efficiency. This SG-PCA method for VRF system sensor FDD is validated using field operation data of the VRF system. Various sensor faults at different fault levels are introduced. The results have showed that the SG-PCA method can significantly improve the fault detection and diagnosis performance compared to conventional PCA method.

Original languageEnglish
Pages (from-to)167-178
Number of pages12
JournalEnergy and Buildings
Volume142
Early online date10 Mar 2017
DOIs
Publication statusPublished - 1 May 2017
Externally publishedYes

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