Adaptive partly-ensemble empirical mode decomposition and its application for rotor rubbing fault diagnosis

作者：Zheng Jinde; Pan Haiyang; Zhang Jun; Cheng Junsheng

来源：Journal of Vibration, Measurement & Diagnosis, 2016, 36(2): 257-263.

DOI：10.16450/j.cnki.issn.1004-6801.2016.02.008

• Empirical Mode Decomposition
• Ensemble empirical mode decomposition
• Ensemble empirical mode decompositions (EEMD)
• High frequency HF
• Intrinsic Mode functions
• Mode mixing
• Permutation entropy
• Rotor rubbings

Ensemble empirical mode decomposition (EEMD) was an effective method for restraining mode mixing of empirical mode decomposition (EMD). However, the effect of EEMD relies on the selection of the size of the added noise, times of iteration and ensemble, and large residual noise will result in an incomplete decomposition. In light of such problems, the adaptive partly ensemble empirical mode decomposition (APEEMD) method was proposed. APEEMD added the adaptive noise in pairs to the targeted signal and automatically set the iteration times for each intrinsic mode IMF). Then, the permutation entropy was employed to detect the high frequency IMFs, and the residual signal was decomposed using EMD. The simulation and rotor rubbing experimental results indicate that the proposed method was effective for fault diagnosis and superior to EEMD in accurate decomposition and inhibition of mode mixing.

• 出版日期2016
• 单位安徽大学