Fault Diagnosis for Variable-Air-Volume Systems Using Fuzzy Neural Networks
This paper presents a new method for fault diagnosis of variable air volume (VAV) air-conditioning systems. The method determines performance indices using selforganizing fuzzy neural networks (SOFNN). The SOFNN has two outstanding characteristics. Firstly, the learning speed is very fast and fuzzy rules can be generated quickly because no iterative learning is employed. Secondly, by using the pruning technology, significant nodes can be self-adaptive according to their contributions to the system performance. Consequently, the proposed method can achieve high performance with a parsimonious structure. Simulation results indicate that the SOFNN-based fault diagnosis method for VAV systems gives a very good performance in training speed and diagnosis speed and has high diagnosis rate.
fault diagnosis VAV air-conditioning system selforganizing fuzzy neural networks
Xie Hui Liu Yan Li Deying
School of Civil and Environment Engineering University of Science and Technology Beijing Beijing, Ch Asset Management Limited University of Science and Technology Beijing Beijing, China School of Environment and Energy Engineering Beijing Institute of Civil Engineering & Architecture B
国际会议
第四届国际计算机新科技与教育学术会议(2009 4th International Conference on Computer Science & Education)
南京
英文
183-188
2009-07-25（万方平台首次上网日期，不代表论文的发表时间）