Generally, the machining vibration frequency spectrum is dominated by the tooth cutting frequency and its harmonics, the part structure and its natural frequency, and the spindle-tool subsystem natural frequency, exhibiting full-oscillatory behaviour. In order to identify the machining status, especially for those thin-walled workpiece machining, the on-machine detected monitoring signals with noise should be decomposed precisely. Actually, the signals’ inherent characteristics, such as the Q-factor, could be employed. In this article, decomposition of the full-oscillatory components from noisy machining vibration signals by minimizing the Q-factor variation is presented. The Q-factor will be calculated using quadratic interpolation of linear prediction coefficients. On this basis, the measured signals can be decomposed into high-, low- and residual-oscillatory signal components using the sparsity-enabled signal analysis. Furthermore, the signal decomposition process is repeated iteratively until the minimization of the Q-factor variation. Finally, the simulation and the thin-walled machining experiments were designed. From comparison of the signal decomposition results with the wavelet packet transform (WPT), it was shown that the signal decomposition accuracy and reliability using the proposed strategy has been improved significantly.