A model of fault feature extraction based on wavelet packet sample entropy and manifold learning

作者：Xiang, Dan; Ge, Shuang

来源：Journal of Vibration and Shock, 2014, 33(11): 1-5.

DOI：10.13465/j.cnki.jvs.2014.11.001

• Classification performance
• Fault feature extractions
• Fault mode identification
• Local tangent space alignment
• Manifold learning
• Sample entropy
• Wavelet Packet
• Wavelet Packet Decomposition

Aiming at nonlinearities of mechanical fault signals, and diversities and complexities of fault symptoms, a model of fault feature extraction was proposed based on wavelet packet sample entropy and manifold learning. Firstly, to extract initial rolling bearing fault features, the sample entropy of the reconstructed signal was calculated with the model by using the wavelet packet decomposition and reconstruction method. Then the local tangent space alignment (LTSA) method for the further extraction of the fault features was applied. The complexity of the feature data was reduced, meanwhile the structural information of the total geometry of the fault features was reserved. Moreover, with the proposed model, the classification performance of the entire fault mode identification was enhanced. Finally, a support vector machine (SVM) was used to classify the fault features extracted with the proposed model. The initial feature extraction and further feature extraction of the classification results were compared to validate the effectiveness of the proposed model.

• 出版日期2014-6-15
• 单位华南理工大学