Reassignment-enable reweighted sparse time-frequency analysis for sparsity-assisted aeroengine rub-impact fault diagnosis

Abstract

Time-frequency analysis (TFA) is an effective tool for the feature extraction of nonstationary signals. However, the energy concentration of the TF representation (TFR) obtained by conventional TFA is weakened when the instantaneous frequency (IF) of signals varies fast, which adversely affects the feature extraction and reconstruction. In this paper, a reassignment-enable reweighted sparse TF method (Re2STF) is proposed to achieve high energy concentration as well as highly accurate reconstruction when analyzing signals with fast-varying IF, and thus is effective to the vibration analysis of aeroengine rub-impact fault. A reweighted sparse TF model is constructed, which iteratively employs the TF reassignment representation into the reweighted strategy to suppress noise and match the fast-varying TF structure better. In particular, the reassignment-enable weight can highlight the TF ridge and suppress the TF coefficients elsewhere. Furthermore, an effective acceleration strategy for the fast iterative shrinkage threshold algorithm is proposed to speed up the model solving. The simulation study and the application in the vibration analysis of a dual-rotor turbofan engine show the improvement of Re2STF in denoising, TF energy concentration and reconstruction, thus proving the effectiveness in aeroengine rub-impact fault diagnosis.