A Study on Fault Diagnosis of Heat Network Leakage Based on GA-ACO-BP Model
Purposes This work has been done in order to overcome the problems of low fault recognition rate, slow convergence speed, and easy falling into local extremum in the current traditional back propagation (BP) model for heat network leakage fault diagnosis. Methods An optimized BP model based on genet...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Editorial Office of Journal of Taiyuan University of Technology
2024-03-01
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| Series: | Taiyuan Ligong Daxue xuebao |
| Subjects: | |
| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2276.html |
| _version_ | 1797208294456033280 |
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| author | Jiangyong HAO Pengfei DUAN Yongfeng DU Mengdan FENG Jinglei CHEN |
| author_facet | Jiangyong HAO Pengfei DUAN Yongfeng DU Mengdan FENG Jinglei CHEN |
| author_sort | Jiangyong HAO |
| collection | DOAJ |
| description | Purposes This work has been done in order to overcome the problems of low fault recognition rate, slow convergence speed, and easy falling into local extremum in the current traditional back propagation (BP) model for heat network leakage fault diagnosis. Methods An optimized BP model based on genetic algorithm-ant colony optimization (GA-ACO) algorithm is proposed. The initial value of the pheromone is improved by using the cross-variance operator of the GA algorithm, the iteration speed of the model and the search for the optimal solution are advanced by using the ACO algorithm, the initial weights and thresholds of the BP model are optimized, and the model is applied to the diagnosis of heat network leakage faults through the system simulation software. Results The results show that compared with traditional BP and GA-BP model, the GA-ACO-BP model possesses faster convergence speed and the predicted value is closer to the expected value with less error, which effectively improves the prediction accuracy of heat network leakage faults and enables fast and accurate diagnosis and localization of leakage faults. |
| first_indexed | 2024-04-24T09:36:31Z |
| format | Article |
| id | doaj.art-783b1587f03d4e40b9d3d937c05cffdd |
| institution | Directory Open Access Journal |
| issn | 1007-9432 |
| language | English |
| last_indexed | 2024-04-24T09:36:31Z |
| publishDate | 2024-03-01 |
| publisher | Editorial Office of Journal of Taiyuan University of Technology |
| record_format | Article |
| series | Taiyuan Ligong Daxue xuebao |
| spelling | doaj.art-783b1587f03d4e40b9d3d937c05cffdd2024-04-15T09:17:32ZengEditorial Office of Journal of Taiyuan University of TechnologyTaiyuan Ligong Daxue xuebao1007-94322024-03-0155233834710.16355/j.tyut.1007-9432.202206061007-9432(2024)02-0338-10A Study on Fault Diagnosis of Heat Network Leakage Based on GA-ACO-BP ModelJiangyong HAO0Pengfei DUAN1Yongfeng DU2Mengdan FENG3Jinglei CHEN4College of Civil Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Civil Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Civil Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Civil Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Civil Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaPurposes This work has been done in order to overcome the problems of low fault recognition rate, slow convergence speed, and easy falling into local extremum in the current traditional back propagation (BP) model for heat network leakage fault diagnosis. Methods An optimized BP model based on genetic algorithm-ant colony optimization (GA-ACO) algorithm is proposed. The initial value of the pheromone is improved by using the cross-variance operator of the GA algorithm, the iteration speed of the model and the search for the optimal solution are advanced by using the ACO algorithm, the initial weights and thresholds of the BP model are optimized, and the model is applied to the diagnosis of heat network leakage faults through the system simulation software. Results The results show that compared with traditional BP and GA-BP model, the GA-ACO-BP model possesses faster convergence speed and the predicted value is closer to the expected value with less error, which effectively improves the prediction accuracy of heat network leakage faults and enables fast and accurate diagnosis and localization of leakage faults.https://tyutjournal.tyut.edu.cn/englishpaper/show-2276.htmlheat network leakagebp neural networkgenetic algorithmant colony optimizationfault diagnosis |
| spellingShingle | Jiangyong HAO Pengfei DUAN Yongfeng DU Mengdan FENG Jinglei CHEN A Study on Fault Diagnosis of Heat Network Leakage Based on GA-ACO-BP Model Taiyuan Ligong Daxue xuebao heat network leakage bp neural network genetic algorithm ant colony optimization fault diagnosis |
| title | A Study on Fault Diagnosis of Heat Network Leakage Based on GA-ACO-BP Model |
| title_full | A Study on Fault Diagnosis of Heat Network Leakage Based on GA-ACO-BP Model |
| title_fullStr | A Study on Fault Diagnosis of Heat Network Leakage Based on GA-ACO-BP Model |
| title_full_unstemmed | A Study on Fault Diagnosis of Heat Network Leakage Based on GA-ACO-BP Model |
| title_short | A Study on Fault Diagnosis of Heat Network Leakage Based on GA-ACO-BP Model |
| title_sort | study on fault diagnosis of heat network leakage based on ga aco bp model |
| topic | heat network leakage bp neural network genetic algorithm ant colony optimization fault diagnosis |
| url | https://tyutjournal.tyut.edu.cn/englishpaper/show-2276.html |
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