Redundant Lifting Wavelet Packet Analysis Based on Variable Parameter and Bearing Fauit Feature Extraction

摘要

A redundant lifting wavelet packet analysis based on variable parameter was presented, which was used to extract the weak fault features of bearing submerged in background noise. Through different choices of parameters in the design formula of predictor based on least square method of fitting, six asymmetric wavelets with various characteristics were constructed, which were then respectively used for redundant lifting wavelet packet decomposition to signal layer by layer. All the results obtained by decomposition were adopted to establish the objective function of minimum norm, through which the optimal wavelet that best matches the feature information is selected for each node signal. The node signals got by last decomposition were utilized for wavelet packet energy analysis, while the node signal with maximum energy was chosen for single branch reconstruction and envelop spectrum analysis. The proposed method above is applied to process the engineering data of bearing with fault and good results are gained by which the effectiveness of this method is well validated.