Identification of a multicomponent signal by a TEA scheme operating in the time-frequency plane

1999 ◽  
Author(s):  
Letizia Lo Presti ◽  
Gabriella Olmo ◽  
Lorenzo Galleani
Keyword(s):  
Time Frequency ◽  
Multicomponent Signal

scholarly journals A Signal Complexity-Based Approach for AM–FM Signal Modes Counting

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2170
Author(s):  
Vittoria Bruni ◽  
Michela Tartaglione ◽  
Domenico Vitulano
Keyword(s):  
Instantaneous Frequency ◽  
Individual Component ◽  
Stationary Process ◽  
Practical Applications ◽  
Time Frequency ◽  
Number Of Components ◽  
Multicomponent Signal ◽  
Multicomponent Signals ◽  
Robustness To Noise ◽  
Signal Complexity

Frequency modulated signals appear in many applied disciplines, including geology, communication, biology and acoustics. They are naturally multicomponent, i.e., they consist of multiple waveforms, with specific time-dependent frequency (instantaneous frequency). In most practical applications, the number of modes—which is unknown—is needed for correctly analyzing a signal; for instance for separating each individual component and for estimating its instantaneous frequency. Detecting the number of components is a challenging problem, especially in the case of interfering modes. The Rényi Entropy-based approach has proven to be suitable for signal modes counting, but it is limited to well separated components. This paper addresses this issue by introducing a new notion of signal complexity. Specifically, the spectrogram of a multicomponent signal is seen as a non-stationary process where interference alternates with non-interference. Complexity concerning the transition between consecutive spectrogram sections is evaluated by means of a modified Run Length Encoding. Based on a spectrogram time-frequency evolution law, complexity variations are studied for accurately estimating the number of components. The presented method is suitable for multicomponent signals with non-separable modes, as well as time-varying amplitudes, showing robustness to noise.

The Certification for Inevitability of Cross-Term in WVD

2014 ◽  
Vol 1030-1032 ◽  
pp. 1930-1933 ◽  
Author(s):  
Zhong Hu Yuan ◽  
Man Yang Xu ◽  
Xiao Xuan Qi
Keyword(s):  
Mathematical Method ◽  
Frequency Analysis ◽  
Cross Term ◽  
Time Frequency Analysis ◽  
Important Method ◽  
Time Frequency ◽  
Multicomponent Signal ◽  
Mathematical Properties ◽  
The Cross

WVD has become an important method for time-frequency analysis because of its mathematical properties. In fact, however, cross-term disturbs its application seriously when the multicomponent signal existed. Now increasing people is going to find pure mathematical method to deal with the cross-term in WVD. But this paper will prove the inevitability of cross-term in WVD.

scholarly journals Time-Frequency Analysis Based on Improved Variational Mode Decomposition and Teager Energy Operator for Rotor System Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Shangkun Liu ◽  
Guiji Tang ◽  
Xiaolong Wang ◽  
Yuling He
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Energy Operator ◽  
Turbine Rotor ◽  
Variational Mode Decomposition ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Multicomponent Signal ◽  
Mode Decomposition ◽  
Teager Energy

A time-frequency analysis method based on improved variational mode decomposition and Teager energy operator (IVMD-TEO) is proposed for fault diagnosis of turbine rotor. Variational mode decomposition (VMD) can decompose a multicomponent signal into a number of band-limited monocomponent signals and can effectively avoid model mixing. To determine the number of monocomponents adaptively, VMD is improved using the correlation coefficient criterion. Firstly, IVMD algorithm is used to decompose a multicomponent signal into a number of monocompositions adaptively. Second, all the monocomponent signals’ instantaneous amplitude and instantaneous frequency are demodulated via TEO, respectively, because TEO has fast and high precision demodulation advantages to monocomponent signal. Finally, the time-frequency representation of original signal is exhibited by superposing the time-frequency representations of all the monocomponents. The analysis results of simulation signal and experimental turbine rotor in rising speed condition demonstrate that the proposed method has perfect multicomponent signal decomposition capacity and good time-frequency expression. It is a promising time-frequency analysis method for rotor fault diagnosis.

scholarly journals A Fast and Robust Spectrogram Reassignment Method

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 358 ◽  
Author(s):  
Vittoria Bruni ◽  
Michela Tartaglione ◽  
Domenico Vitulano
Keyword(s):  
Signal Processing ◽  
Classical Method ◽  
Computational Effort ◽  
Significant Gain ◽  
Simple Method ◽  
Time Frequency ◽  
Multicomponent Signal ◽  
Analysis Window ◽  
Formal Properties ◽  
Selection Of

The improvement of the readability of time-frequency transforms is an important topic in the field of fast-oscillating signal processing. The reassignment method is often used due to its adaptivity to different transforms and nice formal properties. However, it strongly depends on the selection of the analysis window and it requires the computation of the same transform using three different but well-defined windows. The aim of this work is to provide a simple method for spectrogram reassignment, named FIRST (Fast Iterative and Robust Reassignment Thinning), with comparable or better precision than classical reassignment method, a reduced computational effort, and a near independence of the adopted analysis window. To this aim, the time-frequency evolution of a multicomponent signal is formally provided and, based on this law, only a subset of time-frequency points is used to improve spectrogram readability. Those points are the ones less influenced by interfering components. Preliminary results show that the proposed method can efficiently reassign spectrograms more accurately than the classical method in the case of interfering signal components, with a significant gain in terms of required computational effort.

scholarly journals Classification of EEG Signals Using Adaptive Time-Frequency Distributions

2016 ◽  
Vol 23 (2) ◽  
pp. 251-260 ◽  
Author(s):  
Nabeel A. Khan ◽  
Sadiq Ali
Keyword(s):  
Eeg Signals ◽  
Concentrate Energy ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Multicomponent Signal ◽  
Adaptive Kernel ◽  
Adaptive Time ◽  
Frequency Features ◽  
Total Accuracy

Abstract Time-Frequency (t-f) distributions are frequently employed for analysis of new-born EEG signals because of their non-stationary characteristics. Most of the existing time-frequency distributions fail to concentrate energy for a multicomponent signal having multiple directions of energy distribution in the t-f domain. In order to analyse such signals, we propose an Adaptive Directional Time-Frequency Distribution (ADTFD). The ADTFD outperforms other adaptive kernel and fixed kernel TFDs in terms of its ability to achieve high resolution for EEG seizure signals. It is also shown that the ADTFD can be used to define new time-frequency features that can lead to better classification of EEG signals, e.g. the use of the ADTFD leads to 97.5% total accuracy, which is by 2% more than the results achieved by the other methods.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

Time-Frequency Analysis of Partial Discharge Phenomena in SF6 gas using Wavelet Transform

1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Partial Discharge ◽  
Time Frequency Analysis ◽  
Time Frequency
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