Auto-identification of dominant modal parameters from multi-batch signals based on weighted SSA to suppress milling vibration

The modal parameters identified from on-site cutting signals can more truly reflect the dynamics of the machine tool in the operating state. However, due to the spindle rotation and position change of movable parts in the cutting process, modal identification based on on-site cutting vibration signals is interfered with the harmonic frequencies, structural time-varying, artificial analysis, and other uncertain factors. The current modal parameter identification methods cannot realize the auto-identification of machine tool structures simultaneously considering the above factors. Therefore, to realize the auto-identification of structural dominant modal parameters eliminating the interference of harmonic, structural time-varying, and artificial analysis, a new weighted SSA (singular spectrum analysis) method is proposed in this paper. First, multi-batch on-site vibration signals are decomposed to extract the eigenvalue and eigen matrix through singular value decomposition (SVD). Then, based on the variance filtering of principal component analysis, a half principal component analysis is proposed to extract the weighted vector of the eigen matrix. After that, the clustering algorithm is adopted to average the sample set, and the power spectrum curve is modified and reconstructed according to the cluster center. The dominant modal parameters are auto-identified with the reconstructed curve and optimized through the genetic algorithm. Finally, cutting tests are conducted to verify the feasibility and effectiveness of the auto-identification and optimization method.