Construction and application of a bearing fault diagnosis model based on improved GWO algorithm
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School of Mechanical and Electronic Engineering, Shandong Vocational College of Industry, Zibo 256414, China
Submission date: 2024-02-03
Final revision date: 2024-05-22
Acceptance date: 2024-06-11
Online publication date: 2024-07-11
Publication date: 2024-07-11
Corresponding author
Lingbo Jiang   

Shandong Vocational College of Industry
In the study of bearing fault diagnosis, an improved gray wolf optimization algorithm is put forward to optimize the support vector machine model. The model improves the convergence factor of the algorithm, and then optimizes the penalty factor and KF parameters of the support vector machine to enhance the accuracy of fault classification.At the same time, in the problem of fault identification, the introduction of adaptive noise set empirical mode decomposition and the combination of permutation entropy are studied to minimize the impact of noise on the identification model. The experimental outcomes indicated that the algorithm proposed in the study had an average fitness value and a standard deviation fitness value of 0 in the benchmark test function and 94.55% accuracy in overall fault identification. The permutation entropy of the vibration signal in the normal state of the bearing was within the range of [0.13, 0.52], which has a more stable state compared to the fault state. The results show that the improved grey Wolf optimization algorithm is applied to the optimization of support vector machine, combined with the adaptive noise set empirical mode decomposition and permutation entropy, and the identification and classification results of bearing faults are successfully improved.
This research received no external funding.
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