制冷技术2026，Vol.46Issue(2)：16-23,8.DOI:10.3969/j.issn.2095-4468.2026.02.103
基于关键特征选择的冷凝器脏堵故障诊断
Fault Diagnosis of Condenser Fouling and Blockage Based on Key Feature Selection
摘要
Abstract
To address the rise in air conditioning energy consumption induced by condenser fouling and blockage faults of electric vehicles,a fault diagnosis method integrating heat transfer mechanisms and back propagation neural networks is proposed in this paper.Through the analysis and simulation verification of the heat transfer mechanism of the condenser,the heat transfer temperature difference is identified as a sensitive feature parameter to characterize the fault,and the importance of each feature to the heat transfer temperature difference is analyzed based on the knowledge of thermal engineering,and the key feature parameters affecting the heat transfer temperature difference are selected,the back-propagation neural network model is established to predict the heat transfer temperature difference using the normal sample data,and fault diagnosis is carried out through the residual analysis.The results show that the method not only reduces the number of sensor requirements and data storage space,but also trains a model with high prediction accuracy and a coefficient of determination of 0.984,which provides an effective support for the early warning of the condenser dirty plugging fault.关键词
汽车空调/冷凝器脏堵/故障诊断/特征选择/反向传播神经网络Key words
Automotive air conditioning/Condenser fouling/Fault diagnosis/Feature selection/Back-propagation neural network分类
交通工程引用本文复制引用
曲贺,王金锋,陶宏,孙晓琳,彭雨乐,刘延岭,吕天翔..基于关键特征选择的冷凝器脏堵故障诊断[J].制冷技术,2026,46(2):16-23,8.基金项目
"十四五"重点研发项目(No.2023YFD2401304). （No.2023YFD2401304）
