基于连续小波系数非线性流形学习的冲击特征提取方法

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振动与冲击 ›› 2012, Vol. 31 ›› Issue (1) : 106-111,.

论文

基于连续小波系数非线性流形学习的冲击特征提取方法

栗茂林1; 梁霖1,2; 王孙安1; 庄健1

作者信息 +

Mechanical impact faults extraction method based on nonlinear manifold learning for continuous wavelet coefficients

LI Mao-lin1; LIANG Lin1,2; WANG Sun-an1; ZHUANG Jian1

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文章历史 +

摘要

为了提取机械设备故障引发的冲击成分，提出了一种基于连续小波系数非线性流形学习的冲击故障特征提取方法。首先，基于小波熵方法优化出最优的Morlet小波波形参数，实现与冲击特征成分的最佳匹配，获取包含冲击特征信息的最优小波系数矩阵。其次，采用局部切空间排列算法对最优小波系数矩阵进行非线性约简，并基于峭度指标最大化原则，确定出特征空间中的有效低维嵌入，从而提取出最优的冲击故障特征。最后，通过仿真数据和工程实际的应用对比分析，表明该方法采用了局部线性化和全局排列的思想，与线性奇异值分解方法相比，不仅在时域上提取出峭度更大的微弱冲击特征成分，而且在频谱中还提取出了相应的低频故障特征。

Abstract

To acquire the impact component aroused by mechanical fault, a novel feature extraction method based on nonlinear manifold learning for continuous wavelet coefficients is put forward. Firstly, the wavelet entropy method is adopted to optimize the Morlet wavelet shape factor in order to match with the impact components to obtain the optimal continuous wavelet coefficients. Secondly, the nonlinear manifold learning algorithm named local tangent space alignment is used to the reduction analysis of the optimal wavelet coefficients matrix, and according to the principle of the maximum kurtosis index, the low-dimensional embedded vectors which introduced to reflect the impact of the failure are extracted from the global coordinates feature matrix. Finally, the simulation and industrial applications show that this approach, compared with the singular value decomposition, is effective to extract not only the weak periodic impacts with the greater kurtosis in time waveform, but also the fault feature frequency in frequency spectrum.

导出引用

栗茂林;梁霖;王孙安;庄健. 基于连续小波系数非线性流形学习的冲击特征提取方法[J]. 振动与冲击, 2012, 31(1): 106-111,

LI Mao-lin;LIANG Lin;WANG Sun-an;ZHUANG Jian. Mechanical impact faults extraction method based on nonlinear manifold learning for continuous wavelet coefficients[J]. Journal of Vibration and Shock, 2012, 31(1): 106-111,