A GA-BP neural network algorithm for fault detection of transmission tower bolts
BackgroundIn the power system, the identification of the health status of the transmission tower is a daily task that must be performed. In addition, bolt loosening is a common damage mode affecting the main materials of transmission towers. When bolt loosening occurs, it weakens the bearing capacit...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Mechanical Engineering |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmech.2024.1496377/full |
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| author | Ziqiang Lu Pengjie He Huiwei Liu Jie Li Ziying Lu |
| author_facet | Ziqiang Lu Pengjie He Huiwei Liu Jie Li Ziying Lu |
| author_sort | Ziqiang Lu |
| collection | DOAJ |
| description | BackgroundIn the power system, the identification of the health status of the transmission tower is a daily task that must be performed. In addition, bolt loosening is a common damage mode affecting the main materials of transmission towers. When bolt loosening occurs, it weakens the bearing capacity of the transmission tower. If not detected and addressed in a timely manner, serious adverse events, such as tower collapse, may occur which will endanger the normal operation of the power system.MethodsBased on this, in order to ensure the normal operation of the transmission tower and improve the identification effect of bolt loosening, the GP-BP neural network algorithm was applied to the detection process. The feasibility of this algorithm was evaluated through the quantitative analysis of different damage degrees.ResultThe results are as follows: 1) except for the average accuracy rate of substructure 7, which is 89.74%, the identification accuracy of other substructures is more than 90%, indicating that the GA-BP neural network algorithm is effective in identifying the single-damage degree of the tower bolt loosening in the main material; 2) the identification accuracy of double-damage substructure is also more than 90%, indicating that the GA-BP algorithm is effective in identifying the double-damage degree of the tower bolt loosening in the main material.ConclusionIn summary, it can be concluded that both the single- and double-damage degree conditions exhibit a relatively considerable recognition accuracy. In addition, the recognition effect of the algorithm under the double-damage degree condition is better than that of the single-damage degree condition. Therefore, it can be applied in practical projects involving double-damage degree conditions to improve the recognition effect of bolt-loosening faults and provide reliable technical support for the safe operation of transmission equipment. |
| format | Article |
| id | doaj-art-64e9a31afae345758d8b3193fe6ebd5b |
| institution | Kabale University |
| issn | 2297-3079 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Mechanical Engineering |
| spelling | doaj-art-64e9a31afae345758d8b3193fe6ebd5b2025-02-07T11:35:55ZengFrontiers Media S.A.Frontiers in Mechanical Engineering2297-30792025-02-011010.3389/fmech.2024.14963771496377A GA-BP neural network algorithm for fault detection of transmission tower boltsZiqiang LuPengjie HeHuiwei LiuJie LiZiying LuBackgroundIn the power system, the identification of the health status of the transmission tower is a daily task that must be performed. In addition, bolt loosening is a common damage mode affecting the main materials of transmission towers. When bolt loosening occurs, it weakens the bearing capacity of the transmission tower. If not detected and addressed in a timely manner, serious adverse events, such as tower collapse, may occur which will endanger the normal operation of the power system.MethodsBased on this, in order to ensure the normal operation of the transmission tower and improve the identification effect of bolt loosening, the GP-BP neural network algorithm was applied to the detection process. The feasibility of this algorithm was evaluated through the quantitative analysis of different damage degrees.ResultThe results are as follows: 1) except for the average accuracy rate of substructure 7, which is 89.74%, the identification accuracy of other substructures is more than 90%, indicating that the GA-BP neural network algorithm is effective in identifying the single-damage degree of the tower bolt loosening in the main material; 2) the identification accuracy of double-damage substructure is also more than 90%, indicating that the GA-BP algorithm is effective in identifying the double-damage degree of the tower bolt loosening in the main material.ConclusionIn summary, it can be concluded that both the single- and double-damage degree conditions exhibit a relatively considerable recognition accuracy. In addition, the recognition effect of the algorithm under the double-damage degree condition is better than that of the single-damage degree condition. Therefore, it can be applied in practical projects involving double-damage degree conditions to improve the recognition effect of bolt-loosening faults and provide reliable technical support for the safe operation of transmission equipment.https://www.frontiersin.org/articles/10.3389/fmech.2024.1496377/fullGA-BP neural network algorithmbolt-loosening failuredetectionpower transmission and inspectionelectrical automation |
| spellingShingle | Ziqiang Lu Pengjie He Huiwei Liu Jie Li Ziying Lu A GA-BP neural network algorithm for fault detection of transmission tower bolts Frontiers in Mechanical Engineering GA-BP neural network algorithm bolt-loosening failure detection power transmission and inspection electrical automation |
| title | A GA-BP neural network algorithm for fault detection of transmission tower bolts |
| title_full | A GA-BP neural network algorithm for fault detection of transmission tower bolts |
| title_fullStr | A GA-BP neural network algorithm for fault detection of transmission tower bolts |
| title_full_unstemmed | A GA-BP neural network algorithm for fault detection of transmission tower bolts |
| title_short | A GA-BP neural network algorithm for fault detection of transmission tower bolts |
| title_sort | ga bp neural network algorithm for fault detection of transmission tower bolts |
| topic | GA-BP neural network algorithm bolt-loosening failure detection power transmission and inspection electrical automation |
| url | https://www.frontiersin.org/articles/10.3389/fmech.2024.1496377/full |
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