In order to diagnose fault accurately by using vibration signal, a method of gear fault diagnosis based on multiscale fuzzy entropy of EEMD was proposed. Firstly, the vibration signal was decomposed adaptively with ensemble empirical mode decomposition (EEMD) and then the components in different scales of original signal were gained. Because the fuzzy entropy could distinguish the complexity of different signals effectively, the fuzzy entropy of intrinsic mode functions (IMFs) by EEMD was calculated. Thus the complexity metric in different scales of original signal was gained, which could be the feature parameter that described the different states of gear. At last the feature parameters were put into least square support vector machine (LS-SVM) for diagnosing the gear fault. Results of the gear box fault test indicated that the proposed method diagnosed gear fault with high accuracy.
YANG Wang-can,ZHANG Pei-lin,WANG Huai-guang,CHEN Yan-long,SUN Ye-zun.
Gear fault diagnosis based on multiscale fuzzy entropy of EEMD[J]. Journal of Vibration and Shock, 2015, 34(14): 163-167
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