针对奇异值分解（SVD）优化局部均值分解（LMD）方法提取高速电梯滚动导靴振动信号故障特征分量的模态混淆现象，提出一种基于自适应增强小波分解（SSWD）优化LMD的高速电梯滚动导靴振动信号特征提取方法。该方法构建低通滤波器、高通滤波器、小波基函数、尺度函数，利用小波分解（WD）的多分辨率滤波特性将原始信号分解为高频细节特征信号和低频近似信号；对高频细节特征信号进行信号增强、将增强后的高频细节特征信号与低频近似信号进行重构；采用LMD从重构信号中提取能够表征滚动导靴故障特征PF分量，求取PF分量的瞬时Teager能量波形进行对比分析。通过对实际工况信号处理、分析，实验结果表明，相比于SVD优化LMD方法，该方法完整地提取了滚动导靴振动信号的故障特征分量，避免了模态混淆现象出现。

Aiming at the phenomenon of mode mixing in the extraction of fault information from the vibration signal of a high speed elevator rolling guide shoe,by the method of singular value decomposition (SVD) optimizing local mean decomposition (LMD), a feature extraction method based on self-adaptive sharpening wavelet decomposition (SSWD) optimizing LMD was proposed.First of all, the low pass filter, high pass filter, wavelet basis function and scale function were constructed.The original signal was decomposed into a high-frequency detailed feature signal and a low-frequency approximate signal by the multi-resolution filtering characteristics of wavelet decomposition (WD).Then, signal enhancement was done on the high frequency detailed feature components, and the enhanced high frequency detailed characteristic signal and the low frequency approximate signal were reconstructed.Finally, the LMD method was used to extract the fault features’ PF component of the rolling guide shoe from the reconstructed signals.The instantaneous Teager energy waveform of the PF component was obtained for comparative analysis.Through the actual working condition signal processing and analysing, the experimental results show that, compared with the SVD optimizing LMD method, the method completely extracts the fault characteristic components of the vibration signal of the rolling guide shoe, and avoids the phenomenon of modal confusion.