Constructing Condition Monitoring Model of Wind Turbine Blades

被引:8
|
作者
Kuo, Jong-Yih [1 ]
You, Shang-Yi [1 ]
Lin, Hui-Chi [1 ]
Hsu, Chao-Yang [1 ]
Lei, Baiying [2 ,3 ,4 ]
机构
[1] Natl Taipei Univ Technol, Dept Comp Sci & Informat Engn, Taipei 106344, Taiwan
[2] Shenzhen Univ, Hlth Sci Ctr, Sch Biomed Engn, Shenzhen 518037, Peoples R China
[3] Shenzhen Univ, Natl Reg Key Technol Engn Lab Med Ultrasound, Shenzhen 518037, Peoples R China
[4] Shenzhen Univ, Guangdong Key Lab Biomed Measurements & Ultrasoun, Shenzhen 518037, Peoples R China
关键词
anomaly detection; machine learning; wavelet transform; FAULT-DIAGNOSIS;
D O I
10.3390/math10060972
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Wind power has become an indispensable part of renewable energy development in various countries. Due to the high cost and complex structure of wind turbines, it is important to design a method that can quickly and effectively determine the structural health of the generator set. This research proposes a method that could determine structural damage or weaknesses in the blades at an early stage via a model to monitor the sound of the wind turbine blades, so as to reduce the quantity of labor required and frequency of regular maintenance, and to repair the damage rapidly in the future. This study used the operating sounds of normal and abnormal blades as a dataset. The model used discrete wavelet transform (DWT) to decompose the sound into different frequency components, performed feature extraction in a statistical measure, and combined with outlier exposure technique to train a deep neural network model that could capture abnormal values deviating from the normal samples. In addition, this paper observed that the performance of the monitoring model on the MIMII dataset was also better than the anomaly detection models proposed by other papers.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Condition Monitoring and Failure prognostic of Wind Turbine Blades
    Rezamand, Milad
    Kordestani, Mojtaba
    Orchard, Marcos
    Carriveau, Rupp
    Ting, David
    Saif, Mehrdad
    [J]. 2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2021, : 1711 - 1718
  • [2] Condition monitoring of wind turbine blades with FBG sensors
    Kang, HanChul
    Kim, Daegil
    Song, Minho
    [J]. 22ND INTERNATIONAL CONFERENCE ON OPTICAL FIBER SENSORS, PTS 1-3, 2012, 8421
  • [3] A Condition Monitoring System for Blades of Wind Turbine Maintenance Management
    Segovia Ramirez, Isaac
    Gomez Munoz, Carlos Quiterio
    Garcia Marquez, Fausto Pedro
    [J]. PROCEEDINGS OF THE TENTH INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT, 2017, 502 : 3 - 11
  • [4] Fundamentals for remote condition monitoring of offshore wind turbine blades
    McGugan, M.
    Sorensen, B. F.
    [J]. STRUCTURAL HEALTH MONITORING 2007: QUANTIFICATION, VALIDATION, AND IMPLEMENTATION, VOLS 1 AND 2, 2007, : 1913 - 1919
  • [5] Intelligent Condition Monitoring of Wind Turbine Blades: A preliminary approach
    Munteanu, Eugeniu
    Zaporojan, Sergiu
    Dulgheru, Valeriu
    Slavescu, Radu Razvan
    Larin, Vladimir
    Rabei, Ivan
    [J]. 2022 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING, ICCP, 2022, : 9 - 16
  • [6] Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
    Sanati, Hadi
    Wood, David
    Sun, Qiao
    [J]. APPLIED SCIENCES-BASEL, 2018, 8 (10):
  • [7] Experience with bicoherence of electrical power for condition monitoring of wind turbine blades
    Jeffries, WQ
    Chambers, JA
    Infield, DG
    [J]. IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, 1998, 145 (03): : 141 - 148
  • [8] Wind turbine condition monitoring
    Sheng, Shuangwen
    [J]. WIND ENERGY, 2014, 17 (05) : 671 - 672
  • [9] An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades
    Tang, Jialin
    Soua, Slim
    Mares, Cristinel
    Gan, Tat-Hean
    [J]. RENEWABLE ENERGY, 2016, 99 : 170 - 179
  • [10] Condition Monitoring and Damage Location of Wind Turbine Blades by Frequency Response Transmissibility Analysis
    Yang, Wenxian
    Lang, Ziqiang
    Tian, Wenye
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2015, 62 (10) : 6558 - 6564