Valve Fault Diagnosis of Variable Refrigerant Flow System based on Genetic Algorithm and Back Propagation Neural Network

Guo Mengru; Tan Zehan; Chen Huanxin; Guo Yabin; Huang Yao

doi:10.3969/j.issn.0253-4339.2018.02.119

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Valve Fault Diagnosis of Variable Refrigerant Flow System based on Genetic Algorithm and Back Propagation Neural Network

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- Valve Fault Diagnosis of Variable Refrigerant Flow System based on Genetic Algorithm and Back Propagation Neural Network
- Journal of Refrigeration Vol. 39, Issue 2, (2018)
- 作者机构：
  
  1. 华中科技大学
  2. 空调设备及系统运行节能国家重点实验室
  3. 珠海格力电器股份有限公司
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0253-4339.2018.02.119
  CLC：
- Published：2018
- 稿件说明：
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Guo Mengru, Tan Zehan, Chen Huanxin, et al. Valve Fault Diagnosis of Variable Refrigerant Flow System based on Genetic Algorithm and Back Propagation Neural Network[J]. Journal of refrigeration, 2018, 39(2).
DOI：

Guo Mengru, Tan Zehan, Chen Huanxin, et al. Valve Fault Diagnosis of Variable Refrigerant Flow System based on Genetic Algorithm and Back Propagation Neural Network[J]. Journal of refrigeration, 2018, 39(2). DOI： 10.3969/j.issn.0253-4339.2018.02.119.

摘要

针对多联机系统（变制冷剂流量系统）阀类故障的诊断特征变量冗杂、诊断效率低的问题，提出一种复合诊断模型，利用遗传算法在原始特征集中搜索特征子集，与参数优化后的BP神经网络模型结合，对多联机阀类故障进行检测和诊断。本文从原始特征集中优化选择了带有18个特征变量的最优特征子集，用该模型对电子膨胀阀卡死、电子膨胀阀泄露和四通阀故障3种故障进行检测，结果表明：该复合诊断模型对故障检测率提高，其中电子膨胀阀的卡死故障检测率提升8%，整体诊断正确率提高到99.27%；该复合诊断模型大大提高了诊断效率，使测试时间缩短了52.17%，表明该复合诊断模型具有较好的故障诊断效果。

Abstract

Variable refrigerant flow (VRF) valve fault detection and diagnosis usually face the problems of too many features and low efficiency. Therefore

a high-efficiency hybrid model based on a genetic algorithm (GA) and back propagation neural network (BPNN) was proposed. In this hybrid model

the feature subset is extracted from the original feature set of the VRF using the GA

and then the parameter-optimized neural network is used to detect and diagnose VRF valve faults. In this study

the hybrid model was used to detect and diagnose faults with electronic expansion valve sticking

leaking

and a four-way valve. The results showed that the hybrid model proposed in this paper could effectively and reliably diagnose faults. The integrated correct rate of fault diagnosis reached a peak value of 99.27%. In particular

the correct rate of electronic expansion valve sticking fault diagnosis was improved by 8%. In addition

the hybrid model obviously improved the detection and diagnosis efficiency

decreasing the operating time by 52.17%.

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Related Institution

Institute of Refrigeration and Cryogenics ，Zhejiang University

Zhejiang Energy Group Co.，Ltd.

College of Engineering, Peking University

School of Environment and Energy, Inner Mongolia University of Science and Technology

College of Energy and Mechanical Engineering, Shanghai University of Electric Power

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