Application of ITT transform in fault diagnosis of wind turbine rolling bearing

被引：0

作者：

Tang G. ^{[1
]}

Pang B. ^{[1
]}

机构：

[1] School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2017年 / 37卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Fault diagnosis; Rolling bearing; Singular value decomposition; TT transform; Wind turbines;

D O I：

10.16081/j.issn.1006-6047.2017.09.011

中图分类号：

学科分类号：

摘要：

A method based on ITT(Improved Time-Time) transform for diagnosing the faint fault of wind turbine rolling bearing is proposed. The TT transform matrix of 1-dimensional vibration signals of rolling bearing is obtained via TT transform to provide its 2-dimensional TT domain reflection. The diagonal elements of TT transform matrix are extracted to filter the low-frequency interference signals and strengthen the fault feature. Since noise has an important influence on the results of TT transform analysis, the SVD (Singular Value Decomposition) method based on the energy entropy norm is applied to enhance the anti-noise ability of TT transform and realize the faint bearing fault feature extraction in the strong noisy background. The simulative results, experimental results and engineering application demonstrate that, the proposed method can effectively diagnose the fault types of wind turbine rolling bearing. © 2017, Electric Power Automation Equipment Press. All right reserved.

引用

页码：83 / 89

页数：6

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