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

Abstract Facing the characteristics of big data in modern industrial system, manually retrieving the data from many different batches of vibration signals for modal analysis will cause low data utilization and low efficiency. At the same time, under cutting condition, the harmonic component generated by the rotating excitation of machine tool interferes the auto-identification of structural modal parameters from a large number of vibration signals. Therefore, to realize the auto-identification of structural dominant modal parameters from large amounts of different batches vibration data under cutting condition, a new weighted-SSA (singular spectrum analysis) method is proposed in this paper. First, multi batch on-site vibration signals are decomposed, and the eigenvalue and matrix are extracted through singular value decomposition. 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. and clustering is adopted to average the noise reduction signals, to realize the automatic screening of a large amount of data and the automatic distinction of modal parameters. Third, the identified dominant mode of the structure with weak link is optimized through the genetic algorithm, to achieve the suppression of spindle tool system vibration. Finally, the cutting tests are conducted to verify the feasibility and effectiveness of the auto-identification and optimization method.