Optimality property of the Gaussian window spectrogram

Optimality property of the Gaussian window spectrogram

Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duratio...

Fault Diagnosis of Induction Machines in Transient Regime Using Current Sensors with an Optimized Slepian Window

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction 15 machines working in transient regime, when time-frequency analysis tools are used. The proposed 16 method relies on the use of the optimized Slepian window for performing the short time Fourier 17 transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite 18...

A Refined Time-frequency Reassignment Technique Applied to Dolphin Echolocation Signals

Existing time-frequency representations are usually too noisesensitive or has too bad resolution for studying multiple transient signal components of biosonar signals. In this paper we calculate reassigned spectrograms using the first Hermite function as window. The optimal length Hermite function giving the perfectly localized reassignment for a Gaussian function is combined with a novel techn...

Time-Frequency Modeling and Detection of random non-stationary signals for Monitoring Purposes

This paper deals with the modelization and detection of non-stationary random signals in the time-frequency space. A time-frequency random model of signal is derived from a given temporal model. The time model we are interested in consists in a deterministic signal embedded in an additive centered Gaussian perturbation. This Gaussian model is characterized by two parameters, which are the mean ...