Automated Fault Detection and Diagnosis for an Air Handling Unit Based on a GA-Trained RBF Network
The objective of this study is to describe the application of a Radius Basis Function (RBF) network to the problem of automated Fault Detection and Diagnosis (FDD) in the Air Handling Unit (AHU) of a Heating Ventilation Air-Conditioning (HVAC) system. First, We analyze the common AHU faults and their dominant symptoms. Next, An FDD strategy for the AHU is proposed which adopts an RBF network to model the causation of symptoms and faults. Gaussian functions are selected as the basis functions of the hidden layer neurons. The parameters of the Gaussian functions and the weights of the network are obtained by using a novel network training method which combines Genetic Algorithm (GA) and psudo-inverse matrix algorithm. Finally, an automated FDD program in C language based on the proposed strategy is developed. The FDD program is tested with the HVAC system installed in an artificial environment laboratory and successfully identifies each of the seven faults artificially introduced at the test site.
Yonghong Huang Nianping Li Yonghong Huang Yangchun Shi
College of Civil Engineering Hunan University Changsha, Hunan Province, 410082, China College of Energy & Power Engineering Changsha University of Science & Technology Changsha, Hunan Pr
国际会议
2006 International Conference on Communications,Circuits and Systems(第四届国际通信、电路与系统学术会议)
广西桂林
英文
2038-2041
2006-06-25（万方平台首次上网日期，不代表论文的发表时间）