Rotor Fault Diagnosis Based on Clustered Symplectic Geometry Mode Decomposition

Symplectic geometric mode decomposition (SGMD) uses periodic similarity to reconstruct signal components, and requires manual setting of termination conditions, which leads to uncertain decomposition results. To solve this problem, a clustered symplectic geometric modal decomposition (CSGMD) method is proposed in this work. First, the time series signal is transformed to a trajectory matrix, and secondly, the trajectory matrix is transformed to a reconstruction matrix consisting of several groups of initial single component reconstruction matrices. Then, the diagonal averaging method is applied to convert each reconstruction matrix into the corresponding initial components of the one-dimensional time series. Finally, K-means clustering algorithm is used to recombine the initial components to obtain the final symplectic geometric components. Compared with SGMD and variational mode decomposition (VMD), the effective components extracted by CSGMD method has lower distortion degree and frequency confusion degree, less interference components and more obvious improvement of fault impact characteristics. This method can effectively extract rotor fault features and improve the accuracy of rotor fault diagnosis.