1.Faculty of Electrical & Mechanical Engineering, Kunming University of Science & Technology, Kunming 650500, China;
2.Yunnan Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology, Kunming 650500, China
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History+
Received
Revised
Published
2023-01-03
2023-03-14
2023-12-28
Issue Date
2023-12-28
Abstract
Aiming at the problems of strong background noise interference of rolling bearing acoustic signals and difficulty in effectively extracting weak fault characteristic information, and considering the advantages of non-contact measurement of acoustic signals, this paper proposes a composite fault acoustic diagnosis method combining parameter adaptive variational mode decomposition (AVMD) combined with improved multi-point optimal minimum entropy deconvolution adjusted (IMOMEDA). The comprehensive index (CI) is used to solve the problem of adaptive selection of VMD parameters, and the maximum weighted kurtosis (WK) is used to identify the optimal component and reconstruct the signal, so as to enhance the pulse characteristic information related to the fault characteristics. Combined with the IMOMEDA method, the periodic pulse signal is extracted from the reconstruction signal, and the fault characteristic frequency is obtained through envelope demodulation. Simulation signals and experimental signals verify the effectiveness of the proposed method. Compared with the traditional VMD, MOMEDA, VMD-MCKD methods, the superiority of the proposed method is highlighted.
ZHOU Wenjie1,2,ZHOU Jun1,2,LIU Xiaoqin1,2,LIU Tao1,2.
Separation and extraction of composite fault features of rolling bearing acoustic signals based on AVMD-IMOMEDA[J]. Journal of Vibration and Shock, 2023, 42(24): 152-159
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