Data-Driven Diagnosis of Nonlinearly Mixed Mechanical Faults in Wind Turbine Gearbox

被引：24
|
作者：
Yang, Qiang [1 ]
Hu, Chunzhi [1 ]
Zheng, Nenggan [2 ]
机构：
[1] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China
[2] Zhejiang Univ, Qiushi Acad Adv Studies, Hangzhou 310027, Zhejiang, Peoples R China
来源：
IEEE INTERNET OF THINGS JOURNAL | 2018年 / 5卷 / 01期
基金：
美国国家科学基金会;
关键词：
D O I：
10.1109/JIOT.2017.2761891
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This letter proposes an efficient algorithmic solution to diagnose multiple mechanical faults in wind turbine gearbox through source number estimation using an empirical mode decomposition (EMD) and singular value decomposition (SVD) joint approach, and source signal recovery based on short-time Fourier transform (STFT), fuzzy C-means clustering and l(1) norm decomposition. The effectiveness of the solution is validated using real wind turbine measurements under multifault scenarios.
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页码：466 / 467
页数：2
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