一种基于非线性流形学习的故障特征提取模型

PDF(1562 KB)

振动与冲击 ›› 2012, Vol. 31 ›› Issue (23) : 132-136.

论文

一种基于非线性流形学习的故障特征提取模型

蒋全胜1，李华荣1，黄鹏2

作者信息 +

A fault feature extraction model based on nonlinear manifold learningJIANG Quan-sheng1 LI Hua-rong1 HUANG Peng2

JIANG Quan-sheng1 LI Hua-rong1 HUANG Peng2

Author information +

文章历史 +

摘要

流形学习作为一种挖掘高维非线性数据内在几何分布特征的有效方法，可用于故障信号的特征提取。针对机械故障诊断中的非线性、故障征兆复杂的诊断问题，提出了一种基于非线性流形学习的故障特征提取模型。该模型针对采集样本的不同处理情形，分别运用Laplacian Eigenmaps算法及其增量、监督算法，进行故障样本的特征提取与分类，由于采用非线性的维数约简方式，极大地保留了故障信号中的整体几何结构信息，增强了故障模式识别的分类性能。最后通过工程实例应用，表明了所提特征提取模型的可行性和有效性。

Abstract

Manifold learning is one of the effective methods as obtaining the geometric distribution within the high-dimensional nonlinear dataset, which can be used for fault signal feature extraction and diagnosis. To carry out the diagnostic problem of nonlinear, complex failure symptom in the mechanical fault diagnosis, we propose a feature extraction model based on manifold learning method. In the model, aiming to different processing conditions of the collected sample, the Laplacian Eigenmaps and its incremental, supervision algorithm are applied to implementing the feature extraction and classification to fault sample. As a result of the non-linear dimension reduction method, the model greatly retained the overall geometry information in the fault signal, which significantly enhanced the classification performance of fault pattern recognition. The experimental results for fault diagnosis of air compressor demonstrate the feasibility and effectiveness of the proposed model.

导出引用

蒋全胜;李华荣;黄鹏. 一种基于非线性流形学习的故障特征提取模型[J]. 振动与冲击, 2012, 31(23): 132-136

JIANG Quan-sheng LI Hua-rong HUANG Peng. A fault feature extraction model based on nonlinear manifold learningJIANG Quan-sheng1 LI Hua-rong1 HUANG Peng2[J]. Journal of Vibration and Shock, 2012, 31(23): 132-136