Application of SPNGO-VMD-SVM in rolling bearing fault diagnosis

Abstract

Abstract Traditional bearing fault feature extraction and fault classification methods have low recognition accuracy and limited recognition capability in noisy environments. To address this problem, this paper proposes an improved northern eagle algorithm (SPNGO) to optimize the variational modal decomposition (VMD) and support vector machine (SVM) to achieve bearing fault diagnosis. Firstly, to overcome the disadvantages of NGO, such as easy fall into local optimal solutions and slow convergence speed, the Sine Cosine Strategy (SCA) and Position Optimisation Search Algorithm (POS) are introduced to optimize the NGO, and the superiority of the SPNGO algorithm is proved through the comparison of different algorithms. Then, SPNGO-VMD is used to adaptively decompose the vibration signals of faulty bearings and generate multiple modal components IMF. The effective IMF components are screened based on the craggy principle to reconstruct the signals. Finally, the reconstructed feature signals are input into SPNGO-SVM for fault classification and compared with other fault diagnosis models. The study results show that the fault diagnosis accuracy of SPNGO-VMD-SVM can reach 96.67%, which can effectively improve the accuracy of bearing fault diagnosis.