To obtain the fault features of the bearing, a method based on variational mode decomposition (VMD), singular value decomposition (SVD) is proposed for fault diagnosis by Gath–Geva (G–G) fuzzy clustering. Firstly, the original signals are decomposed into mode components by VMD accurately and adaptively, and the spatial condition matrix (SCM) can be obtained. The SCM utilized as the reconstruction matrix of SVD can inherit the time delay parameter and embedded dimension automatically, and then the first three singular values from the SCM are used as fault eigenvalues to decrease the feature dimension and improve the computational efficiency. G–G clustering, one of the unsupervised machine learning fuzzy clustering techniques, is employed to obtain the clustering centers and membership matrices under various bearing faults. Finally, Hamming approach degree between the test samples and the known cluster centers is calculated to realize the bearing fault identification. By comparing with EEMD and EMD based on a recursive decomposition algorithm, VMD adopts a novel completely nonrecursive method to avoid mode mixing and end effects. Furthermore, the IMF components calculated from VMD include large amounts of fault information. G–G clustering is not limited by the shapes, sizes and densities in comparison with other clustering methods. VMD and G–G clustering are more suitable for fault diagnosis of the bearing system, and the results of experiment and engineering analysis show that the proposed method can diagnose bearing faults accurately and effectively.
Rolling bearing fault detection based on local characteristic-scale decomposition and teager energy operator
