IEEE Access (Jan 2024)
A New Method for Bearing Fault Diagnosis Based on Adaptive SVMD and RCMDSE-IDHT
Abstract
In practical engineering, significant noise and amplitude fluctuations in bearing vibration data, hinder the accuracy of fault identification. In order to overcome these difficulties, reduce external interference, and accurately obtain effective bearing fault information in complex environments, a new technique of adaptive SVMD and RCMDSE-IDHT bearing fault diagnosis is proposed. Firstly, the article proposes to adopt the Crested Porcupine Optimizer (CPO) algorithm to optimise the Successive Variational Modal Decomposition (SVMD) parameters to form the adaptive SVMD (ASVMD) algorithm; Subsequently, the original bearing vibration signals are decomposed using the ASVMD to form a number of Intrinsic Mode Function (IMF) signals; Secondly, the Pearson correlation coefficient is used to filter the IMF signal and reconstruct so as to remove the noise from the original signal; In order to further improve the fault identification accuracy, the article proposes to optimise and improve the Differential Symbolic Entropy (DSE) to form the Refined Composite Multi-scale Differential Symbolic Entropy (RCMDSE); and the RCMDSE is used for comprehensive feature extraction of the reconstructed vibration signal; Finally, for the defects of the DHT classifier, the article proposes to adopt the Spearman’s correlation coefficient and the entropy weight method to improve the traditional Mahalanobis Distance (MD) to form the improve Weighted Mahalanobis Distance (IWMD), which uses the advantages of IWMD to improve the Self-organizing Divisive Hierarchical Voronoi Tessellation (DHT) classifier to form improve DHT (IDHT) classifier. In order to test the accuracy as well as the effectiveness of the new method proposed in the article, the bearing data was used for test analysis, and the accuracy of the failure recognition accuracy was as high as 99.237%through test analysis. To verify the superiority of the method mentioned, compared with the six fault recognition models, the results showed that the accuracy of the diagnosis of the new method of faults in this paper increased by 0.72% to 11.75%. At the same time, to validate the extensiveness of the method proposed in this paper, the article utilizes the Southeast University bearing dataset for validation.
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