Reconstruction of Temperature Distribution by Acoustic Tomography Based on Principal Component Analysis and Deep Neural Network
In order to obtain the online monitoring information of boiler furnace temperature field in thermal power plant quickly and accurately, a temperature field reconstruction algorithm of acoustic tomography (AT) based on deep neural network (DNN) was proposed. After normalizing the measured values, com...
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Editorial Department of Power Generation Technology
2023-06-01
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| Series: | 发电技术 |
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| Online Access: | https://www.pgtjournal.com/article/2023/2096-4528/2096-4528-2023-44-3-399.shtml |
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| author | ZHANG Lifeng LI Jing WANG Zhi |
| author_facet | ZHANG Lifeng LI Jing WANG Zhi |
| author_sort | ZHANG Lifeng |
| collection | DOAJ |
| description | In order to obtain the online monitoring information of boiler furnace temperature field in thermal power plant quickly and accurately, a temperature field reconstruction algorithm of acoustic tomography (AT) based on deep neural network (DNN) was proposed. After normalizing the measured values, combined with principal component analysis (PCA) dimension reduction, a fully connected network was constructed to distinguish the peak type. Moreover, DNN and BP neural network were built to predict the normalized slowness value and its maximum value, respectively. Finally, the temperature field distribution was reconstructed. Four typical temperature field models were simulated by using this method. The results show that the reconstruction quality of DNN algorithm is better than that of Tikhonov regularization algorithm and conjugate gradient algorithm. In addition, the average relative error and root mean square error of reconstructed image are less than 0.36% and 0.85% respectively. |
| format | Article |
| id | doaj-art-efefb69f23c54fb58f2c033f8d1c05d8 |
| institution | OA Journals |
| issn | 2096-4528 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Editorial Department of Power Generation Technology |
| record_format | Article |
| series | 发电技术 |
| spelling | doaj-art-efefb69f23c54fb58f2c033f8d1c05d82025-08-20T01:47:25ZengEditorial Department of Power Generation Technology发电技术2096-45282023-06-0144339940610.12096/j.2096-4528.pgt.210842096-4528（2023）03-0399-08Reconstruction of Temperature Distribution by Acoustic Tomography Based on Principal Component Analysis and Deep Neural NetworkZHANG Lifeng0LI Jing1WANG Zhi2Department of Automation, North China Electric Power University, Baoding 071003, Hebei Province, ChinaDepartment of Automation, North China Electric Power University, Baoding 071003, Hebei Province, ChinaDepartment of Automation, North China Electric Power University, Baoding 071003, Hebei Province, ChinaIn order to obtain the online monitoring information of boiler furnace temperature field in thermal power plant quickly and accurately, a temperature field reconstruction algorithm of acoustic tomography (AT) based on deep neural network (DNN) was proposed. After normalizing the measured values, combined with principal component analysis (PCA) dimension reduction, a fully connected network was constructed to distinguish the peak type. Moreover, DNN and BP neural network were built to predict the normalized slowness value and its maximum value, respectively. Finally, the temperature field distribution was reconstructed. Four typical temperature field models were simulated by using this method. The results show that the reconstruction quality of DNN algorithm is better than that of Tikhonov regularization algorithm and conjugate gradient algorithm. In addition, the average relative error and root mean square error of reconstructed image are less than 0.36% and 0.85% respectively.https://www.pgtjournal.com/article/2023/2096-4528/2096-4528-2023-44-3-399.shtmlthermal power plantpower plant boilertemperature fieldacoustic tomography (at)deep neural network (dnn)principal component analysis (pca) |
| spellingShingle | ZHANG Lifeng LI Jing WANG Zhi Reconstruction of Temperature Distribution by Acoustic Tomography Based on Principal Component Analysis and Deep Neural Network 发电技术 thermal power plant power plant boiler temperature field acoustic tomography (at) deep neural network (dnn) principal component analysis (pca) |
| title | Reconstruction of Temperature Distribution by Acoustic Tomography Based on Principal Component Analysis and Deep Neural Network |
| title_full | Reconstruction of Temperature Distribution by Acoustic Tomography Based on Principal Component Analysis and Deep Neural Network |
| title_fullStr | Reconstruction of Temperature Distribution by Acoustic Tomography Based on Principal Component Analysis and Deep Neural Network |
| title_full_unstemmed | Reconstruction of Temperature Distribution by Acoustic Tomography Based on Principal Component Analysis and Deep Neural Network |
| title_short | Reconstruction of Temperature Distribution by Acoustic Tomography Based on Principal Component Analysis and Deep Neural Network |
| title_sort | reconstruction of temperature distribution by acoustic tomography based on principal component analysis and deep neural network |
| topic | thermal power plant power plant boiler temperature field acoustic tomography (at) deep neural network (dnn) principal component analysis (pca) |
| url | https://www.pgtjournal.com/article/2023/2096-4528/2096-4528-2023-44-3-399.shtml |
| work_keys_str_mv | AT zhanglifeng reconstructionoftemperaturedistributionbyacoustictomographybasedonprincipalcomponentanalysisanddeepneuralnetwork AT lijing reconstructionoftemperaturedistributionbyacoustictomographybasedonprincipalcomponentanalysisanddeepneuralnetwork AT wangzhi reconstructionoftemperaturedistributionbyacoustictomographybasedonprincipalcomponentanalysisanddeepneuralnetwork |