An engineering condition indicator for condition monitoring of wind turbine bearings

被引:19
作者
Hu, Aijun [1 ]
Xiang, Ling [1 ]
Zhu, Lijia [1 ]
机构
[1] North China Elect Power Univ, Dept Mech Engn, Beijing 071003, Peoples R China
基金
中国国家自然科学基金;
关键词
bearing; condition monitoring; fault detection; indicator; wind turbine; FAULT-DIAGNOSIS; GEARBOX; SIGNATURE; SYSTEM;
D O I
10.1002/we.2423
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Condition monitoring (CM) of wind turbine becomes significantly important part of wind farms in order to cut down operation and maintenance costs. The large amount of CM system vibration data collected from wind turbines are posing challenges to operators in signal processing. It is crucial to design sensitive and reliable condition indicator (CI) in wind turbine CM system. Bearing plays an important role in wind turbine because of its high impact on downtime and component replacement. CIs for wind turbine bearing monitoring are reviewed in the paper, and the advantages and disadvantages of these indicators are discussed in detail. A new engineering CI (ECI), which combined the energy and kurtosis representation of the vibration signal, is proposed to meet the requirement of easy applicability and early detection in wind turbine bearing monitoring. The quantitative threshold setting method of the ECI is provided for wind turbine CM practice. The bearing run-to-failure experiment data analysis demonstrates that ECI can evaluate the overall condition and is sensitive to incipient fault of bearing. The effectiveness in engineering of ECI is validated though a certain amount of real-world wind turbine generator and gearbox bearing vibration data.
引用
收藏
页码:207 / 219
页数:13
相关论文
共 35 条
  • [1] [Anonymous], 2014, PHM 2014 P ANN C PRO
  • [2] [Anonymous], 2012, SCI TECH INF TECH RE
  • [3] A time-frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions
    Antoniadou, I.
    Manson, G.
    Staszewski, W. J.
    Barszcz, T.
    Worden, K.
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2015, 64-65 : 188 - 216
  • [4] A classifier fusion system for bearing fault diagnosis
    Batista, Luana
    Badri, Bechir
    Sabourin, Robert
    Thomas, Marc
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2013, 40 (17) : 6788 - 6797
  • [5] Monitoring wind turbine gearboxes
    Feng, Yanhui
    Qiu, Yingning
    Crabtree, Christopher J.
    Long, Hui
    Tavner, Peter J.
    [J]. WIND ENERGY, 2013, 16 (05) : 728 - 740
  • [6] Complex signal analysis for wind turbine planetary gearbox fault diagnosis via iterative atomic decomposition thresholding
    Feng, Zhipeng
    Liang, Ming
    [J]. JOURNAL OF SOUND AND VIBRATION, 2014, 333 (20) : 5196 - 5211
  • [7] Condition monitoring of wind turbines: Techniques and methods
    Garcia Marquez, Fausto Pedro
    Mark Tobias, Andrew
    Pinar Perez, Jesus Maria
    Papaelias, Mayorkinos
    [J]. RENEWABLE ENERGY, 2012, 46 : 169 - 178
  • [8] Goyal D, 2016, ARCH COMPUT METHODS, V24, P1
  • [9] Physics of failure approach to wind turbine condition based maintenance
    Gray, Christopher S.
    Watson, Simon J.
    [J]. WIND ENERGY, 2010, 13 (05) : 395 - 405
  • [10] Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods
    Guo, Peng
    Bai, Nan
    [J]. ENERGIES, 2011, 4 (11) : 2077 - 2093