Fault Diagnosis of Rotating Equipment Bearing Based on EEMD and Improved Sparse Representation Algorithm

Lijun Wang, Xiangyang Li, Da Xu, Shijuan Ai, Donglai Xu, Chaoge Wang

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Abstract

Aiming at the problem that the vibration signals of rolling bearings working in a harsh environment are mixed with many harmonic components and noise signals, while the traditional sparse representation algorithm takes a long time to calculate and has a limited accuracy, a bearing fault feature extraction method based on the ensemble empirical mode decomposition (EEMD) algorithm and improved sparse representation is proposed. Firstly, an improved orthogonal matching pursuit (adapOMP) algorithm is used to separate the harmonic components in the signal to obtain the filtered signal. The processed signal is decomposed by EEMD, and the signal with a kurtosis greater than three is reconstructed. Then, Hankel matrix transformation is carried out to construct the learning dictionary. The K-singular value decomposition (K-SVD) algorithm using the improved termination criterion makes the algorithm have a certain adaptability, and the reconstructed signal is
constructed by processing the EEMD results. Through the comparative analysis of the three methods under strong noise, although the K-SVD algorithm can produce good results after being processed by the adapOMP algorithm, the effect of the algorithm is not obvious in the low-frequency range. The method proposed in this paper can effectively extract the impact component from the signal. This will have a positive effect on the extraction of rotating machinery impact features in complex noise environments.
Original languageEnglish
Article number1734
Number of pages25
JournalProcesses
Volume10
Issue number9
DOIs
Publication statusPublished - 1 Sept 2022

Bibliographical note

Funding Information:
This research was supported by the National Natural Science Foundation of China Youth Fund, grant number 62003315; Scientific and Technological Project of Henan Province, grant number 212102210069; Henan Province “ZHONGYUAN Thousand Talent Program”, grant number ZYQR201810075; ZHONGYUAN Talent Program, grant number ZYYCYU202012112; Zhengzhou Measurement and Control Technology and Instrument Key Laboratory, grant number 121PYFZX181; the Applied Basic Research Program of Shanxi Province, grant number 201901D211241; and The Young Academic Leaders Support Program of the North University of China, grant number QX202002.

Publisher Copyright:
© 2022 by the authors.

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