Intelligent wind turbine gearbox diagnosis using VMDEA and ELM

被引:32
|
作者
Isham, Muhammad Firdaus [1 ]
Leong, Muhammad Salman [1 ]
Lim, Meng Hee [1 ]
Bin Ahmad, Zair Asrar [2 ]
机构
[1] Univ Teknol Malaysia, Inst Noise & Vibrat, Kuala Lumpur 54100, Malaysia
[2] Univ Teknol Malaysia, Sch Mech Engn, Skudai, Malaysia
关键词
DE; ELM; fault diagnosis; gearbox; VMD; wind turbine; VARIATIONAL MODE DECOMPOSITION; FAULT-DIAGNOSIS; DIFFERENTIAL EVOLUTION; PLANETARY GEARBOXES; OPTIMIZATION; ALGORITHM; SPECTRUM; SPEED; EMD;
D O I
10.1002/we.2323
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
Wind turbine gearbox diagnosis is a vital tool for maintaining wind turbine operation and safety. The gearbox vibration signal is invariably complex and variable, and useful information and features are difficulty of extraction. Recently, a new and adaptive signal decomposition method, known as variational mode decomposition (VMD), has been proposed, which helps to improve the efficiency and effectiveness of extracting features from gearbox vibration signals. However, the performance of the VMD method mainly depends on its input parameters, especially the mode number and balancing parameter (also called the quadratic penalty term). Hence, this paper proposes a selection method for an optimized VMD parameter using differential evolution algorithm (DEA), also called VMDEA. Firstly, the VMDEA is used to select optimized VMD input parameters for each of the vibration signals. Following this, VMD decomposes each vibration signal into sets of subsignals using the selected optimized parameter. Multidomain features are extracted from VMD reconstructed signals and are passed on to the extreme learning machine (ELM) for fault classification. This study can thus provide a good solution for determining an optimized VMD parameter for decomposing vibration signals and can also provide a more efficient and effective diagnostic approach to wind turbine gearbox maintenance.
引用
收藏
页码:813 / 833
页数:21
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