New method for bearing fault diagnosis based on variational mode decomposition technique

Résumé

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.

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