Diagnosis Model for Refrigerant Charge Fault under Heating Conditions based on Multi-layer Convolution Neural Network
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)...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Journal of Refrigeration Magazines Agency Co., Ltd.
2020-01-01
|
| Series: | Zhileng xuebao |
| Subjects: | |
| Online Access: | http://www.zhilengxuebao.com/thesisDetails#10.3969/j.issn.0253-4339.2020.01.040 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 0253-4339 |