Topic
Time–frequency analysis
About: Time–frequency analysis is a research topic. Over the lifetime, 5407 publications have been published within this topic receiving 104346 citations.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: The new method proposed in this paper addresses both problems using the Gabor analysis of the current via the chirp z-transform, which can be easily adapted to generate high-resolution time-frequency stamps of different types of faults.
Abstract: Time-frequency analysis of the transient current in induction motors (IMs) is the basis of the transient motor current signature analysis diagnosis method. IM faults can be accurately identified by detecting the characteristic pattern that each type of fault produces in the time-frequency plane during a speed transient. Diverse transforms have been proposed to generate a 2-D time-frequency representation of the current, such as the short time Fourier transform (FT), the wavelet transform, or the Wigner-Ville distribution. However, a fine tuning of their parameters is needed in order to obtain a high-resolution image of the fault in the time-frequency domain, and they also require a much higher processing effort than traditional diagnosis techniques, such as the FT. The new method proposed in this paper addresses both problems using the Gabor analysis of the current via the chirp z-transform, which can be easily adapted to generate high-resolution time-frequency stamps of different types of faults. In this paper, it is used to diagnose broken bars and mixed eccentricity faults of an IM using the current during a startup transient. This new approach is theoretically introduced and experimentally validated with a 1.1-kW commercial motor in faulty and healthy conditions.
86 citations
••
TL;DR: Three new techniques for nonstationary signal analysis (the Choi-Williams distribution, a reduced interference distribution, and the Bessel distribution) were tested to determine their advantages and limitations for analysis of the Doppler blood flow signal of the femoral artery.
Abstract: The time-frequency distribution of the Doppler ultrasound blood flow signal is normally computed by using the short-time Fourier transform or autoregressive modeling. These two techniques require stationarity of the signal during a finite interval. This requirement imposes some limitations on the distribution estimate. In the present study, three new techniques for nonstationary signal analysis (the Choi-Williams distribution, a reduced interference distribution, and the Bessel distribution) were tested to determine their advantages and limitations for analysis of the Doppler blood flow signal of the femoral artery. For the purpose of comparison, a model simulating the quadrature Doppler signal was developed, and the parameters of each technique were optimized based on the theoretical distribution. Distributions computed using these new techniques were assessed and compared with those computed using the short-time Fourier transform and autoregressive modeling. Three indexes, the correlation coefficient, the integrated squared error, and the normalized root-mean-squared error of the mean frequency waveform, were used to evaluate the performance of each technique. The results showed that the Bessel distribution performed the best, but the Choi-Williams distribution and autoregressive modeling are also techniques which can generate good time-frequency distributions of Doppler signals. >
85 citations
••
TL;DR: A deep learning-based method by using single channel electroencephalogram (EEG) that automatically exploits the time–frequency spectrum of EEG signal, removing the need for manual feature extraction is developed.
85 citations
••
TL;DR: In this article, a laboratory grinding spindle-typed rotor bearing system was equipped, and modal testing was carried out to identify system characteristics and operating range, and damage detection from nonstationary mechanical vibration signals collected during acceleration and deceleration was performed.
85 citations
••
TL;DR: This decomposition provides a method of parameter simplification which appears to be useful for detecting fundamental frequencies, and characterizing formants.
Abstract: Uses an algorithm based on the adapted-window Malvar transform to decompose digitized speech signals into a local time-frequency representation. The authors present some applications and experimental results for a signal compression and automatic voiced-unvoiced segmentation. This decomposition provides a method of parameter simplification which appears to be useful for detecting fundamental frequencies, and characterizing formants. >
85 citations