Abstract: Aiming at the problem of insufficient feature extraction ability of the symplectic geometric mode decomposition method in decomposing complex signals, a fault diagnosis method based on slip symplectic geometric mode decomposition (SSGMD) was proposed. Firstly, the slip matrix was constructed by adding windows to replace the trajectory matrix, and the ability of periodic feature extraction was enhanced. Secondly, the eigenvalues were obtained by symplectic geometry similarity transformation of the slip matrix, and the initial single component matrix was obtained by reconstructing the eigenvector corresponding to the eigenvalues. Then, a series of initial symplectic geometry components were obtained by diagonally averaging the initial single component matrix. Finally, a series of initial symplectic geometry components were spliced and recombined to obtain sliding symplectic geometry components (SSGCs), and the adaptive decomposition of the signal was completed. The experimental results show that by analyzing the simulated signal and measured planetary gearbox signals, SSGMD can not only protect the structural information of the original signal, but also fully extract the state information of the original signal by using slip matrix and symplectic geometry similarity transformation. Comparing with the classical signal decomposition methods, the proposed SSGMD method can decompose multicomponent signals effectively and has superior feature extraction ability.

Key words:  planetary gearbox; complex signals decompostion; sliding symplectic geometry mode decomposition (SSGMD); feature extraction ability;signal adaptive decomposition;slip matrix