Fault diagnosis of wind turbine gearbox based on the wavelet decomposition and least square support vector machine

ZHANG Jin-min1; ZHAI Yu-qian2; WANG Si-ming2

doi:10.13873/j.1000-97872011.01.006

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Fault diagnosis of wind turbine gearbox based on the wavelet decomposition and least square support vector machine

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- Fault diagnosis of wind turbine gearbox based on the wavelet decomposition and least square support vector machine
- Vol. 30, Issue 1, Pages: 41-43(2011)
- 作者机构：
  
  1. 兰州交通大学机电工程学院
  2. 兰州交通大学自动化与电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872011.01.006
  CLC： TH165.3;TM315
- Published：2011
- 稿件说明：
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[1]张金敏,翟玉千,王思明.小波分解和最小二乘支持向量机的风机齿轮箱故障诊断[J].传感器与微系统,2011,30(01):41-43.

ZHANG Jin-min1, ZHAI Yu-qian2, WANG Si-ming2. Fault diagnosis of wind turbine gearbox based on the wavelet decomposition and least square support vector machine[J]. 2011, 30(1): 41-43.
[1]张金敏,翟玉千,王思明.小波分解和最小二乘支持向量机的风机齿轮箱故障诊断[J].传感器与微系统,2011,30(01):41-43. DOI： 10.13873/j.1000-97872011.01.006.

ZHANG Jin-min1, ZHAI Yu-qian2, WANG Si-ming2. Fault diagnosis of wind turbine gearbox based on the wavelet decomposition and least square support vector machine[J]. 2011, 30(1): 41-43. DOI： 10.13873/j.1000-97872011.01.006.

摘要

根据齿轮箱故障时振动信号特点

提出了一种基于小波分解和最小二乘支持向量机（LS-SVM）相结合的齿轮箱故障诊断方法。通过对齿轮箱振动信号进行小波分解

得到各分解节点对应频率段的重构信号和节点的能量

并将各节点能量组成的特征向量作为诊断模型的特征向量

输入到LS-SVM多类分类器中进行故障识别。诊断结果表明:该方法能够准确地识别风力发电机组齿轮箱的常见故障。

Abstract

A method of fault diagnosis of gearbox based on the wavelet decomposition and least square support vector machine（LS-SVM）is proposed according to characteristics of vibration signal for gearbox.The reconstructed signal at the corresponding frequency ranges and the energy of each decomposed node are obtained by wavelet decomposition of gearbox vibration signal.The eigenvector composed by energy of each node is regarded as the eigenvector of diagnosis models and is put into the LS-SVM multi-classifier to recognize failures.The result of the recognition indicates that the common fault of wind turbine gearbox can be exactly identified by this method.

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references

多类支持向量机方法的研究现状与分析 [J]. 赵春晖,陈万海,郭春燕. 智能系统学报 . 2007(02)

支持向量机和最小二乘支持向量机的比较及应用研究 [J]. 阎威武,邵惠鹤. 控制与决策 . 2003(03)

基于小波神经网络的齿轮故障模式识别 [D]. 李书磊. 武汉科技大学 2007

小波分析在变速箱齿轮故障诊断中的应用研究 [D]. 朱忠奎. 合肥工业大学 2002

支持向量机理论及工程应用实例[M]. 西安电子科技大学出版社 , 白鹏等, 2008

风电场工程技术手册[M]. 机械工业出版社 , 宫靖远主编, 2004

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