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

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 Goshawk Algorithm to optimize the variational modal decomposition (VMD) and support vector machine (SVM) to achieve bearing fault diagnosis. Firstly, to overcome the disadvantages of the Northern Goshawk Algorithm, 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 Northern Goshawk Algorithm. The improved algorithm is called SPNGO for short. The superiority of the SPNGO algorithm is proved by comparing 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 research results show that the proposed SPNGO-VMD-SVM fault diagnosis model is compared with the data set of Case Western Reserve University and the data set of Xi’an Jiaotong University. The diagnostic accuracy of the two groups of experiments can reach 96.67% and 98.89% respectively, and the intelligent diagnosis of different fault states of rolling bearings can be realized simultaneously.