首页|Cyclic symplectic component decomposition with application in planetary gearbox fault diagnosis
Cyclic symplectic component decomposition with application in planetary gearbox fault diagnosis
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
Due to the structural complexity of planetary gearbox, it is difficult to extract the fault information in the vibration signal of planetary gearbox, which seriously affects the accuracy of fault diagnosis. Although the existing fault diagnosis methods, such as local characteristic-scale decomposition (LCD) and ensemble empirical mode decomposition (EEMD), are widely used in the field of fault diagnosis, they are difficult to decompose complex signals effectively. Therefore, a new signal processing method, called cyclic symplectic component decomposition (CSCD) based on the Toeplitz matrix and the cyclic matrix, is proposed in this paper. In CSCD, to obtain the closed-loop solutions, each row is the right cyclic shift of the previous row, so as to avoid information leakage. Meanwhile, the combination of sample covariance and symplectic geometry similarity transformation is constructed to further protect the status information and weaken the influence of noise. In addition, the reconstruction of the same mode is completed by using the extreme difference similarity (EDS), which can realize the adaptive decomposition of the signal. The research results of simulation signal and actual planetary gearbox fault signal show that the proposed CSCD method has superior decomposition performance.
NSTL
Elsevier
