热管/蒸气压缩复合空调系统故障诊断模型分类解释性研究

张义奇; 黄烁全; 历秀明; 狄彦强; 宋孟杰; 韩宗伟

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热管/蒸气压缩复合空调系统故障诊断模型分类解释性研究

更新时间：2025-07-22

- 热管/蒸气压缩复合空调系统故障诊断模型分类解释性研究
- Interpretability Study on the Fault Diagnosis Model of the Heat pipe/ Vapor Compression Composite Air Conditioning System
- 默认刊物名称 2025年
- 作者机构：
  
  1.辽宁省流程工业节能与绿色低碳技术工程研究中心，东北大学,辽宁省沈阳市110819
  2.中国建筑科学研究院,北京市北京市100013
  3.机械与车辆学院，北京理工大学,北京市北京市100081
- 作者简介：
  
  [ "韩宗伟，男，教授，东北大学，。研究方向：高精度控温技术，绿色/高效制冷相关理论及其关键技术。" ]
- 基金信息：
  
  国家自然科学基金(52478081)
- DOI：
  中图分类号： TB657.2
- 收稿日期：2025-06-02，
  
  修回日期：2025-06-10，
  
  录用日期：2025-07-15，
- 稿件说明：
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张义奇, 黄烁全, 历秀明, 等. 热管/蒸气压缩复合空调系统故障诊断模型分类解释性研究[J/OL]. 默认刊物名称, 2025.

ZHANG Yiqi, HUANG Shuoquan, LI Xiuming, et al. Interpretability Study on the Fault Diagnosis Model of the Heat pipe/ Vapor Compression Composite Air Conditioning System[J/OL]. Moren journal, 2025.
张义奇, 黄烁全, 历秀明, 等. 热管/蒸气压缩复合空调系统故障诊断模型分类解释性研究[J/OL]. 默认刊物名称, 2025. DOI：

ZHANG Yiqi, HUANG Shuoquan, LI Xiuming, et al. Interpretability Study on the Fault Diagnosis Model of the Heat pipe/ Vapor Compression Composite Air Conditioning System[J/OL]. Moren journal, 2025. DOI：

摘要

将数据驱动的故障诊断模型用于数据中心空调系统，可有效提高其运行可靠性。然而此类模型通常缺乏诊断依据，限制了其广泛应用。本文建立了基于典型机器学习算法的复合空调系统故障诊断模型，比较了各模型诊断性能，最后基于SHAP（Shapley additive explanation）方法对诊断模型进行了可解释性研究。结果表明，基于卷积神经网络（convolutional neural network，CNN）的故障诊断模型在热管及蒸气压缩模式下性能均为最优，在各分类下F-1值均高于0.999。热管模式下，CNN模型诊断所依据的主要特征为冷凝器风机频率、室外温度及制冷剂泵功耗；在蒸气压缩模式下则为室外温度、压缩机频率和过冷度。

Abstract

Applying data-driven fault diagnosis models to data center air conditioning systems can significantly improve operational reliability. However

such models often lack diagnostic interpretability

limiting their application. This study develops a composite fault diagnosis model based on typical machine learning algorithms

compares the diagnostic performance of different models

and finally conducts interpretability research on the diagnostic models using the SHAP method. The results demonstrate that the CNN-based fault diagnosis model achieves optimal performance in both heat pipe and vapor compression modes

with F-1 scores exceeding 0.999 across all classifications. In heat pipe mode

the diagnosis of CNN model primarily relies on condenser fan frequency

outdoor temperature

and refrigerant pump power consumption as key features

whereas in vapor compression mode

the dominant features are outdoor temperature

compressor frequency

and subcooling degree.

关键词

Keywords

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