Time–frequency analysis of torsional vibration signals based on the improved complete ensemble empirical mode decomposition with adaptive noise, Robust independent component analysis, and Prony’s methods

Abstract

The torsional vibration signals of rotating shafts are multimodal non-stationary noisy signals. The harmonics and attenuation characteristics of these non-stationary signals cannot be obtained effectively by the ordinary short-time Fourier transform algorithm. Although Prony analysis can accurately fit and identify the characteristic coefficients of such non-stationary signals, it is still sensitive to noise. In this article, we propose a system for denoising and identification of torsional vibration signals. In particular, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), wavelet transform, and robust independent component analysis methods are used to denoise the torsional vibration signals, and then, Prony analysis is used to obtain the characteristic parameters of these signals. The proposed algorithm has good denoising performance and it can improve the identification accuracy and reduce the order of the Prony analysis.