Fault diagnosis for refrigeration system based on PCA-PNN

被引：0

作者：

Liang Q. ^{[1
]}

Han H. ^{[1
]}

Cui X. ^{[1
]}

Gu B. ^{[2
]}

机构：

[1] School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai

[2] Institute of Refrigeration and Cryogenics, Shanghai Jiao Tong University, Shanghai

来源：

Huagong Xuebao/CIESC Journal | 2016年 / 67卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Fault diagnosis; Optimization; Principal component analysis; Probabilistic neural network; Refrigeration system;

D O I：

10.11949/j.issn.0438-1157.20151301

中图分类号：

学科分类号：

摘要：

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. © All Right Reserved.

引用

页码：1022 / 1031

页数：9

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