Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion
With the rapid development of social economy, energy consumption is growing tremendously so green energy such as wind energy has become widely used, thus promoting the construction of wind turbines. Due to the long-term use of the electro-mechanical unit, the traditional maintenance cost is too high...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10847805/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825199943405535232 |
|---|---|
| author | Yongjun Qi Hailin Tang Altangerel Khuder |
| author_facet | Yongjun Qi Hailin Tang Altangerel Khuder |
| author_sort | Yongjun Qi |
| collection | DOAJ |
| description | With the rapid development of social economy, energy consumption is growing tremendously so green energy such as wind energy has become widely used, thus promoting the construction of wind turbines. Due to the long-term use of the electro-mechanical unit, the traditional maintenance cost is too high. In order to quickly and accurately detect and maintain the fan blades, based on the intelligent big data from the environment, we propose the convolutional neural network model to solve the problem of low recognition rate due to the lack of feature extraction in the fan blade crack image, and the long short-term memory network (Long Short-Term Memory, LSTM) convolutional neural network model, and the dimensionality reduction of the captured image data, which is beneficial to improve the recognition rate of the picture and reduce the loss rate of the picture through the detection model’s suitable recognition of complex background problems such as target occlusion and overlap. Using LSTM to extract the global context module can effectively improve the target detection accuracy. When this part is added, the detection accuracy will increase by about 3% to 7%. The image position can be accurately captured and the recognition rate is greatly improved through the optimized convolutional neural network, which can provide a reference for future research in other fields. |
| format | Article |
| id | doaj-art-416693c1d5eb4fb3b53e08ce361e617a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-416693c1d5eb4fb3b53e08ce361e617a2025-02-08T00:00:15ZengIEEEIEEE Access2169-35362025-01-0113157621577210.1109/ACCESS.2025.353207710847805Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature FusionYongjun Qi0https://orcid.org/0000-0001-8903-4474Hailin Tang1Altangerel Khuder2https://orcid.org/0000-0001-7697-546XFaculty of Megadata and Computing, Guangdong Baiyun University, Guangzhou, ChinaFaculty of Megadata and Computing, Guangdong Baiyun University, Guangzhou, ChinaSchool of Information and Communication Technology, Mongolian University of Science and Technology, Bayanzurkh, Ulaanbaatar, MongoliaWith the rapid development of social economy, energy consumption is growing tremendously so green energy such as wind energy has become widely used, thus promoting the construction of wind turbines. Due to the long-term use of the electro-mechanical unit, the traditional maintenance cost is too high. In order to quickly and accurately detect and maintain the fan blades, based on the intelligent big data from the environment, we propose the convolutional neural network model to solve the problem of low recognition rate due to the lack of feature extraction in the fan blade crack image, and the long short-term memory network (Long Short-Term Memory, LSTM) convolutional neural network model, and the dimensionality reduction of the captured image data, which is beneficial to improve the recognition rate of the picture and reduce the loss rate of the picture through the detection model’s suitable recognition of complex background problems such as target occlusion and overlap. Using LSTM to extract the global context module can effectively improve the target detection accuracy. When this part is added, the detection accuracy will increase by about 3% to 7%. The image position can be accurately captured and the recognition rate is greatly improved through the optimized convolutional neural network, which can provide a reference for future research in other fields.https://ieeexplore.ieee.org/document/10847805/SCADA databaseneural networkLSTM feature extractionwind turbine blades |
| spellingShingle | Yongjun Qi Hailin Tang Altangerel Khuder Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion IEEE Access SCADA database neural network LSTM feature extraction wind turbine blades |
| title | Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion |
| title_full | Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion |
| title_fullStr | Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion |
| title_full_unstemmed | Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion |
| title_short | Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion |
| title_sort | fan blade crack detection algorithm based on multi scale feature fusion |
| topic | SCADA database neural network LSTM feature extraction wind turbine blades |
| url | https://ieeexplore.ieee.org/document/10847805/ |
| work_keys_str_mv | AT yongjunqi fanbladecrackdetectionalgorithmbasedonmultiscalefeaturefusion AT hailintang fanbladecrackdetectionalgorithmbasedonmultiscalefeaturefusion AT altangerelkhuder fanbladecrackdetectionalgorithmbasedonmultiscalefeaturefusion |