PDF
Full
Outline
On-line and efficient monitoring of leakage faults in district heating network can effectively increase the quality of heat transmission and reduce energy consumption. However, the data feature extraction ability of conventional leakage fault diagnosis method is limited, and it is difficult to deal with the high dimensional nonlinear pressure flow monitoring data for complex heating network, which makes its diagnostic performance weak. Therefore, a fault diagnosis model of heating network leakage based on convolutional neural network (CNN) and Transformer was proposed. The proposed CNN-Transformer diagnostic model combines CNN and Transformer network to realize joint learning of different time scales and spatial features. The CNN network was used to extract local features, and the Transformer network was used to capture global features. The validity of the model was verified by simulating the fault data set of the annular heating pipe network system. The results show that the proposed CNN-Transformer diagnosis model based on multi-stage feature extraction and fusion mechanism of fault features significantly improves the accuracy of leak diagnosis. The CNN-Transformer method has the highest accuracy on the test set, with an accuracy increase of 13.21%, 7.49%, 6.1% and 4.62%, respectively, compared to other fault diagnosis methods including long short-term memory network, gate recurrent network, CNN and Transformer.
PDF
315
117
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
