Synchro-Reassigning Scaling Chirplet Transform for Planetary Gearbox Fault Diagnosis

It is difficult for classic time-frequency analysis (TFA) methods to characterize close-spaced nonlinear frequency curves with satisfactory time-frequency concentration and adaptively decompose above frequency components. As such, a novel TFA technique, termed synchro-reassigning scaling Chirplet transform (SRSCT) is developed in this paper. The proposed SRSCT is inspired by the scaling-basis Chirplet transform (SBCT) and reassignment theory. The novelties of the SRSCT are concluded as (a) a synchro-reassigning operator (SRO) is constructed for adaptively calculating the ideal time-frequency amplitudes on the instantaneous frequency (IF) ridges from the SBCT result and removing other smeared time-frequency coefficients, as a result, time-frequency resolution of the novel time-frequency representation (TFR) is greatly improved, close-spaced frequency curves are clearly characterized and noise interference is eliminated; and (b) perfect adaptive mode decomposition ability is achieved by combining the SRO, ridge detection method and vold-Kalman filter (VKF). Analysis results of the simulated signal show the effectiveness of the proposed SRSCT. Experimental analysis on faulty planetary gearbox signal shows that the proposed method can clearly characterize fault features. Compared with eight classic TFA methods, the Rényi entropies of the TFRs calculated by the proposed method are smallest in two cases, and they are 2.75 and 3.03, respectively, which means that the SRSCT can generate the TFR with the highest energy concentration.