基于多尺度正交PCA-LPP流形学习算法的故障特征增强方法

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (13) : 66-70.

论文

张晓涛，唐力伟，王平，邓士杰

作者信息 +

Fault feature enhancement method based on multiscale orthogonal PCA-LPP manifold learning algorithm

Zhang Xiaotao, Tang Liwei, Wang Ping, Deng Shijie

Author information +

文章历史 +

摘要

针对齿轮箱故障声发射信号特征增强问题，提出一种多尺度正交PCA-LPP非线性流形学习特征增强方法，兼顾PCA的全局方差增大变换特性以及LPP的局部非线性特征保持特性，并通过正交化消除投影分量间的冗余信息，使处理之后的齿轮箱故障信号内含的故障特征得到增强，一方面增强后信号包络谱中的故障谱线清晰明显，另一方面增强后信号以小波包能量熵为特征量，故障类型的辨识率显著提高，可以达到93.75%。

Abstract

Aiming at the feature enhancement problem of gearbox fault acoustic emission signals, a novel method based on multiscale orthogonal PCA-LPP manifold learning algorithm is proposed in this paper by considering the global distribution variance enhancement of PCA and the local nonlinear characteristics enhancement of LPP, and the redundant information between projection components were eliminated by orthogonal treatment. In the processing results of gearbox fault acoustic emission signals, on the one hand fault lines in envelope spectrum was more clearly, on the other hand, the eigenvectors of fault signals was constructed by wavelet packet energy entropy, the fault identification rate was increased obviously, and it could reach 93.75%.

导出引用

张晓涛，唐力伟，王平，邓士杰. 基于多尺度正交PCA-LPP流形学习算法的故障特征增强方法[J]. 振动与冲击, 2015, 34(13): 66-70

Zhang Xiaotao, Tang Liwei, Wang Ping, Deng Shijie. Fault feature enhancement method based on multiscale orthogonal PCA-LPP manifold learning algorithm[J]. Journal of Vibration and Shock, 2015, 34(13): 66-70

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