基于WPD和LPP的设备故障诊断方法研究

小波包分解(WPD)能够将非平稳信号在低频和高频上同时分解以有效反映信号潜在的特征信息，而局部保留投影法(LPP)在降维的同时保留了信号的局部特征信息。结合上述特点，本文给出了选取信号小波包分解后形成全部节点的谱能量，作为表征信号的特征，采用LPP提取降维特征进行模式识别的方法进行设备故障分类研究。本文在多组不同轴承故障及同故障不同损伤程度的多类别数据集上进行了实验，实验结果验证了这种方法的有效性。

Machine Fault Diagnosis Based on WPD and LPP

Wavelet packet decomposition (WPD) can effectively reflect the potential signal characteristics by decomposing the non-stationary signal into the low and high frequencies. Locality preserving projection (LPP) can retain the local features of the analyzed signal in dimensionality reduction. Combining these two benefits, this paper selects the spectral energy of all nodes with WPD as a characterization of the analyzed signal and uses LPP feature extraction to reduce dimensions for pattern recognition of machine faults. Experimental results show the effectiveness of the proposed method by using multiple multi-class dataset of bearing faults with different fault types and defect severities.