Fault diagnosis method of rotating machinery based on both EEMD and fuzzy information entropy

Abstract

Abstract: Aimed at the lower accuracy of fault recognition of rotary machinery, the fault diagnosis method is proposed based on both EEMD and fuzzy information entropy. By using this method and incorporating with the superiority of EEMD and fuzzy information entropy in connection with feature extraction, a feature set is constructed, which can finely measure the complexity of the fault probability of different category of vibration signals. Firstly, the original vibration signal is decomposed with EEMD to obtain several intrinsic modality functions (IMFs), the fuzzy information entropy of the first 5 high-frequency IMF components is calculated to compose high-dimensional feature set then the LPP is used to reduce the dimensionality of the high-dimensional feature set and eliminate redundant irrelevant features, and finally, the reduced sample set is input into the KNN classifier to identify the faults. Foregoing method is validated by data collected from a double-span rotor test rig and compared with the methods of EMD fuzzy entropy, EMD fuzzy information entropy and EEMD fuzzy entropy in connection with the fault recognition accuracy. The result show that this method is able to extract effectively the fault feature of rotor vibration signals.It have higher fault recognition accuracy.

Key words: rotating machinery, fault diagnosis, EEMD, fuzzy entropy, fuzzy information entropy

CLC Number:

TH133
TH165

ZHAO Rong-zhen, ZHANG Chen, DENG Lin-feng. Fault diagnosis method of rotating machinery based on both EEMD and fuzzy information entropy[J]. Journal of Lanzhou University of Technology, 2020, 46(3): 39-44.

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