Diagnosis Model for Refrigerant Charge Fault under Heating Conditions based on Multi-layer Convolution Neural Network

Cheng Hengda; Chen Huanxin; Li Zhengfei; Cheng Xiangdong

doi:10.3969/j.issn.0253-4339.2020.01.040

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Diagnosis Model for Refrigerant Charge Fault under Heating Conditions based on Multi-layer Convolution Neural Network

更新时间：2024-07-18

- Diagnosis Model for Refrigerant Charge Fault under Heating Conditions based on Multi-layer Convolution Neural Network
- Journal of Refrigeration Vol. 41, Issue 1, (2020)
- 作者机构：
  
  1. 华中科技大学能源与动力工程学院
  2. 武汉纺织大学
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0253-4339.2020.01.040
  CLC：
- Published：2020
- 稿件说明：
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Cheng Hengda, Chen Huanxin, Li Zhengfei, et al. Diagnosis Model for Refrigerant Charge Fault under Heating Conditions based on Multi-layer Convolution Neural Network[J]. Journal of refrigeration, 2020, 41(1).
DOI：

Cheng Hengda, Chen Huanxin, Li Zhengfei, et al. Diagnosis Model for Refrigerant Charge Fault under Heating Conditions based on Multi-layer Convolution Neural Network[J]. Journal of refrigeration, 2020, 41(1). DOI： 10.3969/j.issn.0253-4339.2020.01.040.

摘要

本文提出一种基于卷积神经网络的故障诊断模型，并通过正交试验优化了3层网络的卷积核和神经元数目，利用图形化的多联机(VRF)系统制冷剂充注量故障实验数据训练了多层卷积神经网络，评估了本模型的故障诊断性能。结果表明：该“数据图形化-多层卷积神经网络”方法建立的模型能够有效进行多联机制冷剂充注量故障诊断，20个输入特征时，对9类故障诊断总正确率最大为91%，比传统BP神经网络达到更高的诊断精度。该方法首次利用卷积神经网络完成了VRF制冷剂充注量故障诊断，为相关研究的拓展奠定了基础。

Abstract

This paper presents a fault diagnosis model based on a convolution neural network. The kernel size and number of neurons of a3-layerconvolutionnetwork were optimized by an orthogonal experiment method. The performance of the refrigerant charge fault diagnosis model of variable refrigerant flow (VRF) system was evaluated with graphed experimental data. The results show that the model established by the "data graphing & multi-layer convolutional network" method can be effectively used for the refrigerant charge fault diagnosis of the VRF system. With 20 chosen input features

the accuracy of the 9 level refrigerant charge fault diagnosis reached 91%

surpassing the performance of traditional back propagation neural networks(BPNN).This is the first time to achieve VRF system refrigerant charge fault diagnosis by using a convolutional network

laying a foundation for the expansion of related research.

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Related Author

Chen Huanxin

Li Jiong

Hu Wenju

Li Shaobin

Liu Jiangyan

Liu Jiahui

Xu Ling

Fan Yuqiang

Related Institution

State Key Laboratory of Compressor Technology, Hefei General Machinery Research Institute

Beijing University of Civil Engineering and Architecture

Gree Electric Appliances, Inc.Zhuhai

Department of Refrigeration & Cryogenics, Huazhong University of Science and Technology

School of Energy and Power Engineering, University of Shanghai for Science and Technology

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