基于稀疏表示的故障敏感特征提取方法  

Sensitive Feature Extraction of Machine Faults Based on Sparse Representation

作　　者：栗茂林[1,2] 梁霖[1] 王孙安[1] 

机构地区：[1]西安交通大学机械工程学院,西安710049 [2]西安交通大学工程坊,西安710049

出　　处：《机械工程学报》2013年第1期73-80,共8页Journal of Mechanical Engineering

基　　金：国家自然科学基金(51075323);中央高校基本科研业务费专项资金(xjj20100066)资助项目

摘　　要：针对故障诊断中的特征选择问题,提出一种基于非负稀疏表示的低维敏感特征提取方法。为了增强主分量的可解释性,针对L1-范数优化目标,通过权系数的稀疏和非负约束实现非负稀疏主分量的提取。采用主分量特征的累积方差变化率自适应地确定稀疏度,并依据稀疏分量与原始特征少关联的需求确定稀疏分量的数目,实现敏感特征的优化提取。通过仿真数据的分析表明,非负稀疏分量不仅提取出描述原始数据分布的敏感特征,还提高了数据的聚类性能。将该方法应用于滚动轴承的多种故障状态识别中,在由非负稀疏主分量构成的特征空间中,数据的聚类效果优于主分量特征空间;综合分析稀疏参数的选取和敏感特征的提取过程,表明提出的稀疏表示方法不仅能自适应地确定稀疏度,还能有效地获取原始特征的敏感程度,为故障诊断特征提取提供了很好的解决方案。To treat the feature selection for mechanical fault diagnosis, a novel method is proposed to find the low-dimensional non-negation sparse principal component representations from the feature set of the measured signals. In facilitating the interpretation of the extracted principal components, combine non-negative and sparse constraints with the Ll-norm variance, the non-negative sparse components can be selected. The cumulative percentage of variance explained is used to select the optimal spa＇rsity of the principal components, and the number of principal components is decided by the demand of sparsity in fault diagnosis. The experimental results from simulation data and the ball bearing vibration analysis show that the proposed method is more effective for machine fault diagnosis than principal component analysis method. Analysis of the optimal sparsity parameters and the sensitive features, suggests that the proposed method not only self-adaptively obtains the degree of sparsity, but also effectively determines the sensitivity of the original features.

关 键 词：稀疏表示 主分量分析 特征提取 故障诊断 

分 类 号：TH17[机械工程—机械制造及自动化]