Domain-Adversarial Transfer Learning-Driven Adaptive Diagnosis Method for Multi-Faults in Subway Train Air Conditioning Systems

Zhang Tongle; Yang Chuang; Chen Huanxin; Zhang Xue

doi:10.12465/issn.0253-4339.20250614001

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Domain-Adversarial Transfer Learning-Driven Adaptive Diagnosis Method for Multi-Faults in Subway Train Air Conditioning Systems

更新时间：2026-10-13

- Domain-Adversarial Transfer Learning-Driven Adaptive Diagnosis Method for Multi-Faults in Subway Train Air Conditioning Systems
- Journal of Refrigeration Vol. 47, Issue 5, Pages: 23-31(2026)
- 作者机构：
  
  1.华中科技大学中欧清洁与可再生能源学院武汉 430074
  2. 华中科技大学能源与动力工程学院武汉 430074
- 作者简介：
  
  Chen Huanxin， male， professor， School of Energy and Power Engineering， Huazhong University of Science and Technology， 86-27-87558330， E-mail： chenhuanxin@tsinghua.org.cn. Research fields： data mining in heating， ventilation and air-conditioning； fault diagnosis of heating， ventilation and air-conditioning；
- 基金信息：
  
  the National Natural Science Foundation of China(51876070)
- DOI：10.12465/issn.0253-4339.20250614001
  CLC： U270.38⁺3;TP183;TP306⁺.3
- Received：14 June 2025，
  
  Revised：2025-09-04，
  
  Accepted：12 September 2025，
  
  Published：16 October 2026
- 稿件说明：
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张同乐,杨闯,陈焕新等.域对抗迁移学习驱动的地铁列车空调多故障自适应诊断方法[J].制冷学报,2026,47(05):23-31.

Zhang Tongle,Yang Chuang,Chen Huanxin,et al.Domain-Adversarial Transfer Learning-Driven Adaptive Diagnosis Method for Multi-Faults in Subway Train Air Conditioning Systems[J].Journal of Refrigeration,2026,47(05):23-31.
张同乐,杨闯,陈焕新等.域对抗迁移学习驱动的地铁列车空调多故障自适应诊断方法[J].制冷学报,2026,47(05):23-31. DOI： 10.12465/issn.0253-4339.20250614001.

Zhang Tongle,Yang Chuang,Chen Huanxin,et al.Domain-Adversarial Transfer Learning-Driven Adaptive Diagnosis Method for Multi-Faults in Subway Train Air Conditioning Systems[J].Journal of Refrigeration,2026,47(05):23-31. DOI： 10.12465/issn.0253-4339.20250614001.

摘要

地铁空调系统的高效故障诊断对降低能耗和保障乘客舒适度至关重要。本文针对地铁空调运行中特征分布差异大、故障复杂的问题，提出一种基于域对抗神经网络（DANN）的无监督迁移学习故障诊断方法。在多功能综合试验车中采集了冷凝器结垢、通风系统结垢和制冷剂泄漏3种单故障及3种单故障组合的并发故障数据，在不同工况和压缩机频率下进行迁移学习验证，单故障诊断准确率达97.30%~98.90%，并发故障诊断准确率为77.80%~86.70%。使用UMAP非线性降维法与SHAP模型可解释性工具研究了并发故障诊断精度低于单故障诊断精度的深层原理。将DANN与其他2种迁移学习模型对比，波动幅度显著小于对比模型。在特征分布差异较大的迁移学习诊断任务中，诊断准确率波动幅度仅为1%，在并发故障特征重叠时仍保持较高诊断精度。

Abstract

Efficient fault diagnosis of metro train air-conditioning systems is essential for reducing energy consumption and ensuring passenger comfort. This paper proposes an unsupervised transfer learning method based on a domain-adversarial neural network （DANN） to address the challenges involving diverse feature distributions and complex faults. Data obtained from three single faults （condenser fouling， ventilation fouling， and refrigerant leakage） and their concurrent combinations are collected from a multifunctional test vehicle under various operating conditions and compressor frequencies. The DANN achieved accuracy values ranging from 97.30%-98.90% for single faults and 77.80%-86.70% for concurrent faults. Uniform manifold approximation and projection （UMAP） and SHapley Additive exPlanations （SHAP） analyses revealed the underlying reasons for the less accurate concurrent fault diagnosis. Compared with the two baseline transfer learning models， DANN exhibited markedly smaller performance fluctuations， maintaining high accuracy， even under large feature distribution shifts and overlapping concurrent fault features.

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references

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