Rolling bearing fault extraction based on adaptive VMD and MOMEDA

Home > Archive>Volume 45, Issue 1, 2022 >165-171

Rolling bearing fault extraction based on adaptive VMD and MOMEDA
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                        Ruan QiangRuan Qiang
1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China； 2. Engineering Research Center for Intelligent Maintenance of Advanced Equipment of Yunnan Province, Kunming 650500, China
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Wang GuiyongWang Guiyong
1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China； 2. Engineering Research Center for Intelligent Maintenance of Advanced Equipment of Yunnan Province, Kunming 650500, China
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Liu TaoLiu Tao
1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China； 2. Engineering Research Center for Intelligent Maintenance of Advanced Equipment of Yunnan Province, Kunming 650500, China
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Wang TingxuanWang Tingxuan
1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China； 2. Engineering Research Center for Intelligent Maintenance of Advanced Equipment of Yunnan Province, Kunming 650500, China
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Affiliation:1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China; 2. Engineering Research Center for Intelligent Maintenance of Advanced Equipment of Yunnan Province, Kunming 650500, China
Clc Number:TH133.3; TH165. 3
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Abstract:

Aiming at the problem that the weak fault features of rolling bearings are difficult to extract, a bearing fault feature extraction method based on the combination of parameter adaptive optimization variable modal decomposition (VMD) and multi-point optimal minimum entropy deconvolution (MOMEDA) is proposed. Firstly, the VMD decomposition is performed on the rolling bearing time domain vibration signal, and then the best mode component (BIMF) is selected based on the principle of maximizing the index of impulse harmonic noise ratio (AIHN) of autocorrelation function and MOMEDA filtering is performed on it, and the fault characteristic frequency is obtained after envelope deconvolution, and finally the fault characteristic frequency can be clearly observed by applying the proposed method body to the numerical simulation signal 131.1Hz, which can be applied to the actual bearing fault signal to effectively identify the bearing fault characteristic frequency of 294.5Hz, which is closer to the theoretical fault characteristic frequency 294Hz compared with 311Hz extracted by the original envelope spectrum and 320Hz extracted by MCKD.

Key words:bearing fault diagnosis;VMD algorithm;MOMEDA algorithm;AIHN algorithm;Parameter optimization

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Online: June 19,2024
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