ENHANCED VRF SYSTEM SENSOR FAULT DETECTION AND DIAGNOSIS USING SAVITZKY-GOLAY AND PRINCIPAL COMPONENT ANALYSIS METHODS
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.To improve the fault detection and diagnosis(FDD)performance,this paper presented 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.In order to determine parameters of the SG method,an optimization index was proposed,which is obtained 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 was validated using field operation data of an existing VRF system.Various sensor faults at different fault levels were introduced.The results showed that the SG-PCA method can significantly improve the fault detection and diagnosis performance compared to conventional PCA method.
Principal component analysis Satizky-Golay method Variable refrigerant flow Sensor fault detection and diagnosis Optimal index
Yabin Guo Huanxin Chen Guannan Li Yunpeng Hu Haorong Li
Department of Refrigeration & Cryogenics,Huazhong University of Science and Technology,Wuhan,China Department of Building Environment and Energy Application Engineering,Wuhan Business University,816 Durham School of Architectural Engineering and Construction College of Engineering,University of Neb
国内会议
珠海
英文
8-17
2016-11-01（万方平台首次上网日期，不代表论文的发表时间）