基于连续小波系数非线性流形学习的冲击特征提取方法

Mechanical impact feature extraction method based on nonlinear manifold learning of continuous wavelet coefficients

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。

To acquire an impact component aroused by mechanical fault, a novel feature extraction method based on nonlinear manifold learning of continuous wavelet coefficients was put forward. Firstly, the wavelet entropy method was 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 was used to reduce the optimal wavelet coefficients matrix, and according to the principle of the maximum kurtosis index, the low-dimensional embedded vectors introduced to reflect impact failures were extracted from the global coordinate feature matrix. Finally, simulations and industrial applications showed that compared with the singular value decomposition, this approach is effective to extract not only the weak impacts with the greater kurtosis in time waveform, but also the fault feature frequencies in frequency spectrum.