化工学报2016，Vol.67Issue(3)：1022-1031,10.DOI:10.11949/j.issn.0438-1157.20151301
基于主元分析-概率神经网络的制冷系统故障诊断
Fault diagnosis for refrigeration system based on PCA-PNN
摘要
Abstract
The diversity of internal physical form of refrigeration system and the deep coupling between the system parameters make the system more intricate and the detection and diagnosis more complicated. Seven typical degrading faults of a refrigeration system, including system-level and component-level, were explored. The principal component analysis (PCA) was applied to extract the principal characters and reduce the dimension of faults samples. The probabilistic neural network (PNN) was used for fault diagnosis. The PCA could decompose the original 62 parameters into independent principal components and select a certain amount of principal components according to the cumulative contributions. Import these principal components as input data into PNN for fault diagnosis. Results indicate that the PNN combined with PCA is not sensitive to the spread value within a certain range. The combination also increased the correct rate and saved the elapsed time of diagnosis. Obviously, the use of PCA could effectively optimize the diagnosis performance of PNN.关键词
主元分析/概率神经网络/制冷系统/故障诊断/优化Key words
principal component analysis/probabilistic neural network/refrigeration system/fault diagnosis/optimization分类
通用工业技术引用本文复制引用
梁晴晴,韩华,崔晓钰,谷波..基于主元分析-概率神经网络的制冷系统故障诊断[J].化工学报,2016,67(3):1022-1031,10.基金项目
国家自然科学基金项目（51506125）。@@@@supported by the National Natural Science Foundation of China (51506125) （51506125）
