New method for bearing fault diagnosis based on variational mode decomposition technique
1
Laboratory of Electromechanical Systems, Department of Electromechanics, Faculty of Science and Technology, Badji Mokhtar-Annaba University, Annaba, Algeria
2
Mathematics and Their Interactions Laboratory, Abdelhafid Boussouf University Center of Mila, Algeria
3
University of Science and Technology Houari-Boumédiène, Algeria
4
Catholic University of Louvain, Belgium
5
Research Center in Industrial Technologies CRTI P.O. Box 64 Cheraga, 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.
Laboratoire de Mathématiques et leurs interactions, Abdelhafid Boussouf University Center of Mila, Algeria. Innovation Academy Mila, Algeria.
Diagnostyka 2024;25(2):2024208
KEYWORDS
vibration signalroot mean squarevariational mode decompositioncross correlationBearing fault diagnosis
TOPICS
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.
ACKNOWLEDGEMENTS
R&D activities have been realized within Innovation Academy Mila in collaboration with Annaba University and University Center of Mila. the authors are grateful to prof Tahar Kezai president of Innovation Academy for their continue supports and advices.
FUNDING
This research received no external funding.
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