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.

Spectral features for the classification of partial discharge signals from selected insulation defect models

Abstract: Time-domain features of partial discharge (PD) signals are often used to classify PD patterns. This paper proposes spectral features that are extracted using a filter bank, consisting of band-pass filters. By applying the fast Fourier transform to the PD signal, the resulting frequency bins are grouped into L octave frequency sub-bands. Two new features called the octave frequency moment coefficients (OFMC) and octave frequency Cepstral coefficients (OFCC) are defined in this paper. In addition, time-frequency domain coefficients (TFDC) obtained via wavelet analysis are also analysed. A PD signal can now be represented as an L -dimensional feature vector of OFMC, OFCC or TFDC. These features are compared with discrete wavelet transform-based higher-order statistical features (HOSF) using three different classifiers: probabilistic neural network, support vector machine and the recently emerged sparse representation classifier. Results show that the proposed spectral features are robust and provide a better classification accuracy of PD signals, compared with HOSF.

Synchrosqueezed wavelet transform for frequency and damping identification from noisy signals

Abstract: Identification of vibration parameters from the analysis of the dynamic response of a structure plays a key role in current health monitoring systems. This study evaluates the capabilities of the recently developed Synchrosqueezed Wavelet Transform (SWT) to extract instant frequencies and damping values from the simulated noise-contaminated response of a structure. Two approaches to estimate the modal damping ratio from the results of the SWT are presented. The results obtained are compared to other signal processing methods based on Continuous Wavelet (CWT) and Hilbert-Huang (HHT) transforms. It was found that the time-frequency representation obtained via SWT is sharped than the obtained using just the CWT and it allows a more robust extraction of the individual modal responses than using the HHT. However, the identification of damping ratios is more stable when the CWT coefficients are employed.

Classification of time-frequency representations based on two-direction 2DLDA for gear fault diagnosis

Abstract: Time-frequency representations (TFR) have been intensively employed for analyzing vibration signals in mechanical faults diagnosis. However, in many applications, time-frequency representations are simply utilized as a visual aid to be used for vibration signal analysis. It is very attractive to investigate the utility of TFR for automatic classification of vibration signals. A key step for this work is to extract discriminative parameters from TFR as input feature vector for classifiers. This paper contributes to this ongoing investigation by developing a two direction two dimensional linear discriminative analysis (TD-2DLDA) technique for feature extraction from TFR. The S transform, which combines the separate strengths of the short time Fourier transform and wavelet transforms, is chosen to perform the time-frequency analysis of vibration signals. Then, a novel feature extraction technique, named TD-2DLDA, is proposed to represent the time-frequency matrix. As opposed to traditional LDA, TD-2DLDA is directly conduct on 2D matrices rather than 1D vectors, so the time-frequency matrix does not need to be transformed into a vector prior to feature extraction. Therefore, the TD-2DLDA can reduce the computation cost and preserve more structure information hiding in original 2D matrices compared to the LDA. The promise of our method is illustrated by performing our procedure on vibration signals measured from a gearbox with five operating states. Experimental results indicate that the TD-2DLDA obviously outperforms related feature extraction schemes such as LDA, 2DLDA in gear fault diagnosis.

Acousto-optic processors for real-time generation of time-frequency representations.

Abstract: Acousto-optic processors for calculating different two-dimensional (2-D) time–frequency representations for one-dimensional temporal signals in real time are described. The various 2-D representations discussed in the literature, such as the Wigner distribution and the ambiguity function, are shown to be obtainable through minor variations in an acousto-optic processor consisting of two Bragg cells in a parallel configuration. Also obtained and discussed are two new time–frequency representations that, for amplitude-modulated signals, correspond to mean-frequency-selective correlation and Doppler-frequency-selective convolution. Experimental results are presented to highlight the special features of the different time–frequency representation.