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      A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

      1 , 2 , 1 , 2 , 1 , 2 , 1 , 2
      Mathematical Problems in Engineering
      Hindawi Limited

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          Abstract

          The nonstationary components and noises contained in the bearing vibration signal make it particularly difficult to extract fault features, and minimum entropy deconvolution (MED), maximum correlated kurtosis deconvolution (MCKD), and fast spectral kurtosis (FSK) cannot achieve satisfactory results. However, the filter size and period range of multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) need to be set in advance, so it is difficult to achieve satisfactory filtering results. Aiming at these problems, a parameter adaptive MOMEDA feature extraction method based on grasshopper optimization algorithm (GOA) is proposed. Firstly, the multipoint kurtosis (MKurt) of MOMEDA filtered signal is used as the optimization objective, and the optimal filter size and periodic initial value which matched with the vibration signal can be determined adaptively through multiple iterations of GOA. Secondly, the periodic impact contained in the vibration signal is extracted by the optimized MOMEDA, and the fault features in the impact signal are extracted by Hilbert envelope demodulation. Finally, the simulation signal and bearing signal are processed by the proposed approach. The results show that the introduction of GOA not only solves the problem of parameter selection in MOMEDA, but also achieves better performance compared with other optimization methods. Meanwhile, the feasibility and superiority of the approach are fully proved by comparing it with the three methods MED, MCKD, and FSK after parameter optimization. Therefore, this approach provides a novel way and solution for fault diagnosis of the rolling bearing, gear, and shaft.

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          ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD

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            Variational Mode Decomposition

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              Grasshopper Optimisation Algorithm: Theory and application

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                Author and article information

                Journal
                Mathematical Problems in Engineering
                Mathematical Problems in Engineering
                Hindawi Limited
                1024-123X
                1563-5147
                March 05 2019
                March 05 2019
                : 2019
                : 1-22
                Affiliations
                [1 ]Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
                [2 ]Engineering Research Center for Mineral Pipeline Transportation of Yunnan Province, Kunming 650500, China
                Article
                10.1155/2019/7182539
                1f491aaf-8b7e-4522-be5e-d7970e2d9e3a
                © 2019

                http://creativecommons.org/licenses/by/4.0/

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