Parameterized Synchrosqueezing Transform With Application to Machine Fault Diagnosis

Abstract

The time–frequency analysis (TFA) is a robust technique for instantaneous frequency (IF) estimation and component separation of nonstationary signal. In this paper, a new method called parameterized synchrosqueezing transform (PST) is proposed. The PST introduces a two-step algorithm, including parameters estimation and synchrosqueezing method, to achieve a compact time–frequency representation (TFR) while enabling the reconstruction of the signal from TFR. First, the IF is obtained by detecting ridges in the time–frequency plane where the signal concentrates most of its energy. A new algorithm called ridges detection algorithm (RDA) is designed in this paper to identify these ridges. The second step is accomplished by computing more accurate estimates of the local 2D IF of the components, which allow the synchrosqueezing transform to relocate the energy distribution. The results of both synthetic and real-world signals verify the efficient performance of the proposed algorithm. Moreover, the practical application of the method to analyze vibration signal demonstrates that the PST possesses a great potential for fault diagnosis in the rotating machine.