An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing

Guoping An, Qingbin Tong, Yanan Zhang, Ruifang Liu, Weili Li, Junci Cao and Yuyi Lin
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Guoping An: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Qingbin Tong: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Yanan Zhang: State Grid JIBEI Electric Power Co., Ltd. Maintenance Branch State Grid, Beijing 102488, China
Ruifang Liu: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Weili Li: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Junci Cao: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Yuyi Lin: Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO 65211, USA

Energies, 2021, vol. 14, issue 4, 1-24

Abstract: The fault diagnosis of rolling element bearing is of great significance to avoid serious accidents and huge economic losses. However, the characteristics of the nonlinear, non-stationary vibration signals make the fault feature extraction of signal become a challenging work. This paper proposes an improved variational mode decomposition (IVMD) algorithm for the fault feature extraction of rolling bearing, which has the advantages of extracting the optimal fault feature from the decomposed mode and overcoming the noise interference. The Shuffled Frog Leap Algorithm (SFLA) is employed in the optimal adaptive selection of mode number K and bandwidth control parameter α. A multi-objective evaluation function, which is based on the envelope entropy, kurtosis and correlation coefficients, is constructed to select the optimal mode component. The efficiency coefficient method (ECM) is utilized to transform the multi-objective optimization problem into a single-objective optimization problem. The envelope spectrum technique is used to analyze the signals reconstructed by the optimal mode components. The proposed IVMD method is evaluated by simulation and practical bearing vibration signals under different conditions. The results show that the proposed method can improve the decomposition accuracy of the signal and the adaptability of the influence parameters and realize the effective extraction of the bearing vibration signal.

Keywords: variational mode decomposition; feature extraction; rolling element bearings; shuffled frog leaping algorithm; envelope entropy; efficiency coefficient method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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