A time-frequency sparse strategy based on optimal flux atom and scale lp approximation operator

Abstract

Periodic impulse features caused by damage to rotating mechanical components are often overwhelmed by redundant components, which seriously affect the fault detection and diagnosis of equipment. Therefore, the time-frequency sparse (TFS) strategy based on optimal flux atom (OFA) and scale lp approximation operator(lp-AO) is proposed to extract periodic fault features. The OFA is determined by short-time Fourier transform (STFT) and correlation analysis of the signals. The convolutional coefficients are obtained by one-dimensional convolutional denoising based on the optimal flux atom (ODCDN-OFA). The convolution coefficients retain the main timing features of the signal. The scale lp-AO sparse model extracts the main frequency features of the convolutional coefficients in the frequency domain. The solution of the lp-AO sparse model relies on the Iterative Reweighed Least Squares (IRLS) algorithm. The effectiveness of the TFS is demonstrated by the analysis of simulated and several experimental signals. The two methods of fast spectral kurtosis (SK) and lq sparse model are used as comparisons. The TFS is demonstrated to be more effective for extracting periodic fault features.

A weighted sparse classification method based on period analysis dictionary

“…We choose the frequency with the largest amplitude in the spectrum as the parameter f, because the frequency of the signal has the property of energy aggregation. The maximum value as the wavelet center frequency allows the constructed atoms to be closer to the time-frequency characteristics of the signal, while revealing the details and features of the signal more accurately [29]. ACF was used to determine the remaining three parameters.…”