Feature Extraction for Incipient Fault Diagnosis of Rolling Bearings Based on Nonlinear Manifold Learning

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Feature Extraction for Incipient Fault Diagnosis of Rolling Bearings Based on Nonlinear Manifold Learning

更新时间：2025-07-09

- Feature Extraction for Incipient Fault Diagnosis of Rolling Bearings Based on Nonlinear Manifold Learning
- Vol. 44, Issue 5, Pages: 45-49(2010)
- 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：
- DOI：
  CLC： TH17
- Online First：10 May 2010，
  
  Published：2010
- 稿件说明：
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Feature Extraction for Incipient Fault Diagnosis of Rolling Bearings Based on Nonlinear Manifold Learning[J]. 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

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