Research on damage detection technology for wind turbine blade acoustic signals by fusion of sparse representation, compressive sensing and deep learning
Abstract In view of the problem that the high noise and data redundancy in the voiceprint signal of the wind turbine blade lead to insufficient diagnostic accuracy and real-time performance and increase the acquisition cost, this paper combines sparse representation, compressed sensing, and deep lea...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
|
| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-025-00382-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849764241409572864 |
|---|---|
| author | Liang Wang Chun Yang Chao Yuan Yanan Liu Yanqing Chen |
| author_facet | Liang Wang Chun Yang Chao Yuan Yanan Liu Yanqing Chen |
| author_sort | Liang Wang |
| collection | DOAJ |
| description | Abstract In view of the problem that the high noise and data redundancy in the voiceprint signal of the wind turbine blade lead to insufficient diagnostic accuracy and real-time performance and increase the acquisition cost, this paper combines sparse representation, compressed sensing, and deep learning technology to apply a new wind turbine blade damage detection method, aiming to enhance the accuracy and real-time performance of wind turbine blade damage diagnosis. The sparse representation method is used to effectively encode the voiceprint signal and extract representative signal features; the compressed sensing technology is applied to efficiently reconstruct the signal using a small amount of sampled data, significantly reducing the data collection amount and storage requirements; deep feature learning and damage pattern classification based on convolutional neural network further improve the accuracy and intelligence level of detection.The research results show that the proposed method effectively reduces the computational complexity and greatly improves the detection accuracy. The accuracy is not less than 88% under five damage types: crack, corrosion, deformation, fatigue and impact. It has good adaptability under different computing resources, and the processing delay does not exceed 0.45s under complex environments and large data volumes. It has strong real-time performance and application potential. |
| format | Article |
| id | doaj-art-ed520248d27e4b6c8fb7f300fa77e887 |
| institution | DOAJ |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-ed520248d27e4b6c8fb7f300fa77e8872025-08-20T03:05:10ZengSpringerDiscover Artificial Intelligence2731-08092025-07-015112410.1007/s44163-025-00382-xResearch on damage detection technology for wind turbine blade acoustic signals by fusion of sparse representation, compressive sensing and deep learningLiang Wang0Chun Yang1Chao Yuan2Yanan Liu3Yanqing Chen4Jiangsu Fangtian Power Technology Co., LTDJiangsu Fangtian Power Technology Co., LTDJiangsu Fangtian Power Technology Co., LTDJiangsu Fangtian Power Technology Co., LTDJiangsu Fangtian Power Technology Co., LTDAbstract In view of the problem that the high noise and data redundancy in the voiceprint signal of the wind turbine blade lead to insufficient diagnostic accuracy and real-time performance and increase the acquisition cost, this paper combines sparse representation, compressed sensing, and deep learning technology to apply a new wind turbine blade damage detection method, aiming to enhance the accuracy and real-time performance of wind turbine blade damage diagnosis. The sparse representation method is used to effectively encode the voiceprint signal and extract representative signal features; the compressed sensing technology is applied to efficiently reconstruct the signal using a small amount of sampled data, significantly reducing the data collection amount and storage requirements; deep feature learning and damage pattern classification based on convolutional neural network further improve the accuracy and intelligence level of detection.The research results show that the proposed method effectively reduces the computational complexity and greatly improves the detection accuracy. The accuracy is not less than 88% under five damage types: crack, corrosion, deformation, fatigue and impact. It has good adaptability under different computing resources, and the processing delay does not exceed 0.45s under complex environments and large data volumes. It has strong real-time performance and application potential.https://doi.org/10.1007/s44163-025-00382-xWind turbine bladesAcoustic signalSparse representationCompressed sensingDeep learning |
| spellingShingle | Liang Wang Chun Yang Chao Yuan Yanan Liu Yanqing Chen Research on damage detection technology for wind turbine blade acoustic signals by fusion of sparse representation, compressive sensing and deep learning Discover Artificial Intelligence Wind turbine blades Acoustic signal Sparse representation Compressed sensing Deep learning |
| title | Research on damage detection technology for wind turbine blade acoustic signals by fusion of sparse representation, compressive sensing and deep learning |
| title_full | Research on damage detection technology for wind turbine blade acoustic signals by fusion of sparse representation, compressive sensing and deep learning |
| title_fullStr | Research on damage detection technology for wind turbine blade acoustic signals by fusion of sparse representation, compressive sensing and deep learning |
| title_full_unstemmed | Research on damage detection technology for wind turbine blade acoustic signals by fusion of sparse representation, compressive sensing and deep learning |
| title_short | Research on damage detection technology for wind turbine blade acoustic signals by fusion of sparse representation, compressive sensing and deep learning |
| title_sort | research on damage detection technology for wind turbine blade acoustic signals by fusion of sparse representation compressive sensing and deep learning |
| topic | Wind turbine blades Acoustic signal Sparse representation Compressed sensing Deep learning |
| url | https://doi.org/10.1007/s44163-025-00382-x |
| work_keys_str_mv | AT liangwang researchondamagedetectiontechnologyforwindturbinebladeacousticsignalsbyfusionofsparserepresentationcompressivesensinganddeeplearning AT chunyang researchondamagedetectiontechnologyforwindturbinebladeacousticsignalsbyfusionofsparserepresentationcompressivesensinganddeeplearning AT chaoyuan researchondamagedetectiontechnologyforwindturbinebladeacousticsignalsbyfusionofsparserepresentationcompressivesensinganddeeplearning AT yananliu researchondamagedetectiontechnologyforwindturbinebladeacousticsignalsbyfusionofsparserepresentationcompressivesensinganddeeplearning AT yanqingchen researchondamagedetectiontechnologyforwindturbinebladeacousticsignalsbyfusionofsparserepresentationcompressivesensinganddeeplearning |