The improved variational nonlinear chirplet mode decomposition via local maximum synchrosqueezing transform and recursive mode extracting scheme for robust estimation of nonlinear chirplet modes and application to fault detection of rotary machine

Abstract

As an advanced time-frequency (TF) decomposition (TFD) method, variational nonlinear chirplet mode decomposition (VNCMD) decomposes the original signal into a series of nonlinear chirplet modes (NCMs), such that the inherent characteristic information contained in the signal can be revealed effectively. However, the decomposition ability of VNCMD is largely affected by the prior instantaneous frequency (IF) and the pre-set parameters. In practical engineering applications, the presence of noise and interference components often complicates the accurate determination of prior IFs and appropriate decomposition parameters. Considering the above issues, in order to precisely extract the NCMs and realize the effective analysis of mechanical vibration signals, this paper mainly focuses on the drawbacks of accurate prior IF and the decomposition parameters of VNCMD, and proposed an improved version via local maximum synchrosqueezing transform and a recursive mode extracting scheme. The performance of the proposed method is evaluated through simulation cases, and the results demonstrate its effectiveness. Finally, the proposed method is successfully applied to bearing data analysis and rub-impact fault detection.