Evidence-Driven and Neural Network-Based Fault Monitoring and Diagnosis Technology for District Cooling and Heating System

Cen Xiaotong; Wang Xi; Hou Hongjuan; Xu Baoping

doi:10.12465/issn.0253-4339.20241224001

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Evidence-Driven and Neural Network-Based Fault Monitoring and Diagnosis Technology for District Cooling and Heating System

更新时间：2026-03-12

- Evidence-Driven and Neural Network-Based Fault Monitoring and Diagnosis Technology for District Cooling and Heating System
- Journal of Refrigeration Vol. 47, Issue 1, Pages: 138-146(2026)
- 作者机构：
  
  1.华北电力大学能源动力与机械工程学院北京 102206
  2. 华北电力大学新能源电力系统国家重点实验室北京 102206
- 作者简介：
- 基金信息：
  
  the National Basic Research Program of China(2021YFE0194500)
- DOI：10.12465/issn.0253-4339.20241224001
  CLC： TP306⁺.3;TU833
- CSTR：XXXXX.XX.XXX.20241224001
- Received：24 December 2024，
  
  Revised：2025-03-11，
  
  Accepted：12 March 2025，
  
  Published：16 February 2026
- 稿件说明：
移动端阅览
岑晓彤,王锡,侯宏娟等.基于证据驱动和神经网络的区域供冷/热系统故障监测及诊断技术[J].制冷学报,2026,47(01):138-146.

Cen Xiaotong,Wang Xi,Hou Hongjuan,et al.Evidence-Driven and Neural Network-Based Fault Monitoring and Diagnosis Technology for District Cooling and Heating System[J].Journal of Refrigeration,2026,47(01):138-146.
岑晓彤,王锡,侯宏娟等.基于证据驱动和神经网络的区域供冷/热系统故障监测及诊断技术[J].制冷学报,2026,47(01):138-146. DOI： 10.12465/issn.0253-4339.20241224001. CSTR： XXXXX.XX.XXX.20241224001.

Cen Xiaotong,Wang Xi,Hou Hongjuan,et al.Evidence-Driven and Neural Network-Based Fault Monitoring and Diagnosis Technology for District Cooling and Heating System[J].Journal of Refrigeration,2026,47(01):138-146. DOI： 10.12465/issn.0253-4339.20241224001. CSTR： XXXXX.XX.XXX.20241224001.

摘要

及时识别出区域供冷/热系统因发生泄漏而导致的故障状态并诊断出泄漏的具体位置和程度可以保障系统的安全运行。为了解决传统基于数据驱动的方法过度依赖数据质量和数量的问题，提高故障监测与诊断模型的鲁棒性，本文构建了一种基于证据驱动和神经网络的区域供冷/热系统故障监测及诊断模型。该模型可以利用基于证据KNN分类器的故障监测模型对系统运行状态进行监测，再通过基于神经网络的故障诊断模型来确定系统的泄漏位置与泄漏量。以河北承德地区一实际区域供热系统为案例对模型开展研究与分析，结果显示：该方法对区域供冷/热系统的故障监测及诊断的准确率为95.8%。

Abstract

The timely identification of the leakage and diagnosis of the specific location and degree of leakage can guarantee safe system operation of district cooling and heating systems. This study proposes an evidence-driven and neural network-based fault-monitoring and diagnosis method for district cooling and heating systems to mitigate the problem of traditional data-driven methods that rely on the quality and quantity of data， improving the robustness of the fault-monitoring and diagnosis model. The method utilizes a fault-monitoring model based on evidence-based K-nearest neighbor classifiers to monitor the system-operation status， and determines the specific leakage location and leakage amount through a neural-network-based leakage fault-diagnosis model. An actual district heating system in Chengde， Hebei Province， is used as a case study， and the results show that the accuracy of the method for fault monitoring and the diagnosis of district cooling/heating systems is 95.8%.

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references

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