自适应邻域构造流形学习算法及故障降维诊断
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中图分类号:

TH165; TN911

基金项目:

国家自然科学基金资助项目(50775219)


Adaptive Neighborhood Selection Manifold Learning Algorithm and Its Application in Fault Diagnosis with Dimensionality Reduction
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    摘要:

    针对流形学习算法中近邻构造问题,提出一种自适应邻域构造方法,该方法基于马氏距离计算样本间相似系数,由相似系数均值确定初始近邻数,根据样本高斯核概率密度估计调整近邻数,并将自适应邻域构造方法用于改进的主成分分析联合局部保持投景(principal component analysis-locality preserving projections,简称PCA-LPP)流形学习算法中。通过齿轮箱故障类型识别对其特征降维性能进行验证,结果表明,自适应邻域PCA-LPP方法比传统的k近邻方法及原始无处理的特征识别率都高,可以达到94.67%。

    Abstract:

    In light of neighborhood selection of a manifold learning algorithm, a novel adaptive neighborhood selection method was proposed. The adjacency matrix was calculated by Mahalanobis distance, then used to obtain the initial neighborhood. The final neighborhood was obtained through regulation of the initial neighborhood using Gauss kernel density estimation of all samples. The improved principal component analysis locality preserving projection (PCA-LPP) manifold learning algorithm using adaptive neighborhood selection was applied in the processing of gearbox fault signals. The results showed that the eigenvectors obtained by adaptive neighborhood selection had a more satisfying fault identification rate than that obtained by k neighborhood and original eigenvectors, reaching as high as 94.67%.

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  • 在线发布日期: 2017-01-11
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