基于主元分析-概率神经网络的制冷系统故障诊断

doi:10.11949/j.issn.0438-1157.20151301

化工学报 ›› 2016, Vol. 67 ›› Issue (3): 1022-1031.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

梁晴晴¹, 韩华¹, 崔晓钰¹, 谷波²

1. 上海理工大学能源与动力工程学院, 上海 200093;
2. 上海交通大学制冷与低温工程研究所, 上海 200240

收稿日期:2015-08-13 修回日期:2015-10-19 出版日期:2016-03-05 发布日期:2016-01-12
通讯作者: 崔晓钰
基金资助:
国家自然科学基金项目(51506125)。

Fault diagnosis for refrigeration system based on PCA-PNN

LIANG Qingqing¹, HAN Hua¹, CUI Xiaoyu¹, GU Bo²

1. School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
2. Institute of Refrigeration and Cryogenics, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2015-08-13 Revised:2015-10-19 Online:2016-03-05 Published:2016-01-12
Contact: 67
Supported by:
supported by the National Natural Science Foundation of China (51506125).

摘要/Abstract

摘要：

制冷系统由于内部物质形态的多样性以及系统参数间的高度耦合而较为复杂,也增加了出现故障后的检测及诊断难度。针对制冷系统常见的7种故障,包括局部故障与系统故障,运用主元分析法提取故障样本主要特征,对样本进行降维处理后,基于概率神经网络进行故障诊断。主元分析法可将原始的62个参数分解为相互独立的主元,根据累计贡献率选取一定量的主元,并将其样本输入概率神经网络进行故障诊断,结果表明结合主元分析后的概率神经网络在一定范围内对spread值不敏感,不仅诊断正确率有所提高,而且缩短了诊断耗时。可见,主元分析法的使用可有效优化概率神经网络的诊断性能。

关键词: 主元分析, 概率神经网络, 制冷系统, 故障诊断, 优化

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.

Key words: principal component analysis, probabilistic neural network, refrigeration system, fault diagnosis, optimization

中图分类号:

TB65

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.

LIANG Qingqing, HAN Hua, CUI Xiaoyu, GU Bo. Fault diagnosis for refrigeration system based on PCA-PNN[J]. CIESC Journal, 2016, 67(3): 1022-1031.

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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

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