利用非线性流形学习的轴承早期故障特征提取方法

栗茂林 1; 王孙安 1; 梁霖 1; 2

您当前的位置：

首页 >

文章列表页 >

利用非线性流形学习的轴承早期故障特征提取方法

更新时间：2025-07-09

- 利用非线性流形学习的轴承早期故障特征提取方法
- Feature Extraction for Incipient Fault Diagnosis of Rolling Bearings Based on Nonlinear Manifold Learning
- 西安交通大学学报 2010年44卷第5期页码：45-49
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2009-10-20. 作者简介: 栗茂林(1978-),女,讲师,在职博士生. 基金项目: 国家自然科学基金资助项目(50505034)
- DOI：
  中图分类号： TH17
- 网络首发：2010-05-10，
  
  纸质出版：2010
- 稿件说明：
移动端阅览
栗茂林 1, 王孙安 1, 梁霖 1, 等. 利用非线性流形学习的轴承早期故障特征提取方法[J]. 西安交通大学学报, 2010,44(5):45-49.

Feature Extraction for Incipient Fault Diagnosis of Rolling Bearings Based on Nonlinear Manifold Learning[J]. 2010, 44(5): 45-49.
栗茂林 1, 王孙安 1, 梁霖 1, 等. 利用非线性流形学习的轴承早期故障特征提取方法[J]. 西安交通大学学报, 2010,44(5):45-49. DOI：

Feature Extraction for Incipient Fault Diagnosis of Rolling Bearings Based on Nonlinear Manifold Learning[J]. 2010, 44(5): 45-49. DOI：

摘要

针对早期故障微弱特征难以提取的问题

提出了一种基于非线性流形学习的滚动轴承早期故障特征提取方法.在由时域指标和小波频带能量组成的原始特征空间中

结合局部切空间排列学习算法的特点

采用散布矩阵分类测度指标

实现了局部邻域的优化选取

从而提取出最优的敏感故障特征.通过实例应用

表明该方法有效地克服了主分量分析和非线性核主分量分析方法的不足

提取的融合特征敏感性更好

从而提高了故障模式的分类性能

实现了轴承的早期故障诊断.

Abstract

To extract the incipient fault feature of rolling bearings

a novel feature extraction approach based on the nonlinear manifold learning algorithm is proposed. Constructing the original feature space with the time domain indexes and the wavelet frequency domain energies

the local target space alignment algorithm is employed for extracting nonlinear low dimensional manifold. According to the characteristics of the scatter matrix classification measure

the selection criterion of local neighborhood parameter is introduced to acquire the sensitive fault features. The experimental results for fault diagnosis of rolling bearings show that this approach

compared with the linear principal component analysis and nonlinear kernel principal component analysis

is more effective to extract the fault features from vibration signals

and enhances the classification ability of failure pattern.

关键词

Keywords

references

费晓琪,孟庆丰,何正嘉.基于冲击时频原子的匹配追踪信号分解及机械故障特征提取技术 [J].振动与冲击,2003,22(2):26-29.

FEI Xiaoqi, MENG Qingfeng, HE Zhengjia.Signal decomposition with matching pursuits and technology of extracting machinery fault feature based on impulse time-frequency atom [J].Journal of Vibration and Shock,2003,22(2):26-29.

邵毅敏,周晓君,欧家福,等.增强型滤波及冲击性机械故障特征的提取 [J].机械工程学报,2009,45(4):166-171.

SHAO Yimin, ZHOU Xiaojun, OU Jiafu, et al.Extracting impact feature of machine fault by using enhanced filtering [J].Journal of Mechanical Engineering,2009,45(4):166-171.

胡桥,何正嘉,张周锁,等.基于提升小波包变换和集成支持矢量机的早期故障智能诊断 [J].机械工程学报,2006,42(8):16-22.

HU Qiao, HE Zhengjia, ZHANG Zhousuo, et al.Intelligent diagnosis for incipient fault based on lifting wavelet package transform and support vector machines ensemble [J].Chinese Journal of Mechanical Engineering,2006,42(8):16-22.

李常有,徐敏强,郭耸.声信号的主分量分析应用于滚动轴承故障诊断 [J].声学技术,2008,27(2):271-275.

LI Changyou, XU Minqiang, GUO Song.Fault diagnosis of rolling element bearing based on principal component analysis of acoustic signal [J].Technical Acoustics,2008,27(2):271-275.

黎敏,徐金梧,阳建宏,等.一种基于流形拓扑结构的轴承故障分类方法 [J].控制工程,2009,16(3):358-362.

LI Min, XU Jinwu, YANG Jianhong, et al.Classification method of bearing faults based on topological structure of manifold [J].Control Engineering of China,2009,16(3):358-362.

蒋全胜,贾民平,胡建中,等.基于拉普拉斯特征映射的故障模式识别方法 [J].系统仿真学报,2008,20(20):5710-5715.

JIANG Quansheng, JIA Minping, HU Jianzhong, et al.Method of fault pattern recognition based on Laplacian eigenmaps [J].Journal of System Simulation,2008,20(20):5710-5715.

ZHANG Z Y, ZHA H Y. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment [J]. SIAM Journal of Scientific Computing, 2004,26(1):313-338.

KOUROPTEVA O, OKUN O, HADID A, et al. Beyond locally linear embedding algorithm,MVG-01-2002 [R].Finland: Machine Vision Group,University of Oulu,2002:1-49.

The Case Western Reserve University Bearing Data Center. Bearing data center fault test data [EB/OL].[2009-10-01].http:∥www.eecs.cwru.edu/laboratory/bearing/.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

冲击故障特征提取的非线性流形学习方法

滚动轴承故障诊断的多小波谱峭度方法

独立分量分析和流形学习在VSC-HVDC系统故障诊断中的应用

滚动轴承故障诊断的案例推理方法

冲击特征受控极小化通用稀疏表示及其在机械故障诊断中的应用