New method for bearing fault diagnosis based on variational mode decomposition technique
 
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1
Laboratoire des systèmes électromécaniques (LSEM), Badji Mokhtar University, Annaba, Algeria. Innovation Academy Mila, Algeria.
 
2
Laboratoire de Mathématiques et leurs interactions, Abdelhafid Boussouf University Center of Mila, Algeria. Innovation Academy Mila, Algeria.
 
3
Innovation Academy Mila, Algeria.
 
4
Research Center in Industrial Technologies CRTI P.O. Box 64 Cheraga, Algeria
 
5
Laboratoire des systèmes électromécaniques (LSEM), Badji Mokhtar University, Annaba, Algeria
 
These authors had equal contribution to this work
 
 
Submission date: 2023-11-20
 
 
Final revision date: 2024-01-29
 
 
Acceptance date: 2024-04-03
 
 
Online publication date: 2024-04-04
 
 
Publication date: 2024-04-04
 
 
Corresponding author
Farida Medjani   

Laboratoire de Mathématiques et leurs interactions, Abdelhafid Boussouf University Center of Mila, Algeria. Innovation Academy Mila, Algeria.
 
 
 
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ABSTRACT
Variational Mode Decomposition (VMD) is a useful tool for decomposing complex multi-component signals. However, one major drawback of VMD is the need to accurately determine the value of sub-signals (IMFs) before starting the process of segmentation. In fact, achieving optimal reconstruction of the denoised original signals depends on the determining optimal number of IMFs (K). This requirement poses a challenge in the capability of analyzing non-stationary or noisy signals. In this paper, a new approach to optimize the variational mode decomposition technique is proposed. This approach automatically estimates the optimal K and also effectively detects the characteristic frequencies associated with faulty bearings. This method is a combination of two algorithms which are based on cross-correlation and root mean square (RMS) statistical analysis. To confirm the efficacy of the proposed method, the bearing vibration dataset from the Case School of Engineering are used. Then, the K obtained through the proposed method are compared with other methods. The results demonstrate that the proposed approach exhibits superior robustness and precision when autonomously evaluating the optimal K for effective identification of bearing fault.
eISSN:2449-5220
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