Downsampling-based synchrosqueezing transform and its applications on large-scale vibration data

Synchrosqueezing transform (SST) is a useful tool for vibration signal analysis due to its high time-frequency concentration and reconstruction properties. However, existing SST requires much processing time and memory for large-scale data. In this paper, some efficient implementation methods of SST based on downsampled short-time Fourier transform are proposed. By controlling the downsampling factor both in time and frequency, combined with the proposed selective reassignment and frequency subdivision scheme, one can keep a balance between efficiency and accuracy according to practical needs. Moreover, the reconstruction property is also available under the downsampling scheme. The effects of parameters on the concentration, computing efficiency, and reconstruction accuracy are also investigated quantitatively, followed by a mathematic model of reassignment behavior affected by decimating factors. Experimental results on an aero-engine and a spindle show that the downsampling-based SST can effectively characterize the non-stationary features of the large-scale vibration data to reveal the mechanism of mechanical systems.