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

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.

Window Processing of EEG Records by the Prony’s Method

The paper considers the application of the Prony analysis for processing digital data in a sliding window. The stages of the algorithm are stated; the basic equations are given. On a model example, the features of constructing the Prony spectra are shown. Particular attention is paid to biometric data (EEG recordings). For them, the method of windowing with the extraction and visualization of complex roots is discussed. The possibility of using window processing to identify mental correlates in EEG records is shown and substantiated. Isolation of features in individual sections of the EEG can be of significant importance in the problems of biometric identification