Spectrum estimation of short-time stationary signals in additive noise and channel distortion

2001 ◽  
Vol 49 (7) ◽  
pp. 1409-1420 ◽  
Author(s):  
Yunxin Zhao
Keyword(s):  
Additive Noise ◽  
Spectrum Estimation ◽  
Stationary Signals ◽  
Channel Distortion ◽  
Short Time

Adaptive stochastic resonance in bistable system driven by noisy NLFM signal: phenomenon and application

2021 ◽  
Vol 379 (2192) ◽  
pp. 20200239 ◽  
Author(s):  
Chen Yang ◽  
Jianhua Yang ◽  
Dengji Zhou ◽  
Shuai Zhang ◽  
Grzegorz Litak
Keyword(s):  
Stochastic Resonance ◽  
Bistable System ◽  
Nonlinear Frequency ◽  
Bearing Fault Diagnosis ◽  
Mode Function ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Fourier Transform Spectrum ◽  
Short Time ◽  
Sr Method

The stochastic resonance (SR) in a bistable system driven by nonlinear frequency modulation (NLFM) signal and strong noise is studied. Combined with empirical mode decomposition (EMD) and piecewise idea, an adaptive piecewise re-scaled SR method based on the optimal intrinsic mode function (IMF), is proposed to enhance the weak NLFM signal. At first, considering the advantages of EMD for dealing with non-stationary signals, the segmented NLFM signal is processed by EMD. Meanwhile, the cross-correlation coefficient is used as the measure to select the optimal IMF that contains the NLFM signal feature. Then, the spectral amplification gain indicator is proposed to realize the adaptive SR of the optimal IMF of each sub-segment signal and reconstruct the enhanced NLFM signal. Finally, the effectiveness of the proposed method is highlighted with the analysis of the short-time Fourier transform spectrum of the simulation results. As an application example, the proposed method is verified adaptability in bearing fault diagnosis under the speed-varying condition that represents a typical and complicated NLFM signal in mechanical engineering. The research provides a new way for the enhancement of weak non-stationary signals. This article is part of the theme issue ‘Vibrational and stochastic resonance in driven nonlinear systems (part 1)’.

scholarly journals Classification of Hydroacoustic Signals Based on Harmonic Wavelets and a Deep Learning Artificial Intelligence System

2020 ◽  
Vol 10 (9) ◽  
pp. 3097
Author(s):  
Dmitry Kaplun ◽  
Alexander Voznesensky ◽  
Sergei Romanov ◽  
Valery Andreev ◽  
Denis Butusov
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Feature Space ◽  
Roc Curves ◽  
Harmonic Wavelets ◽  
Intelligence System ◽  
Stationary Signals ◽  
Harmonic Wavelet ◽  
Short Time

This paper considers two approaches to hydroacoustic signal classification, taking the sounds made by whales as an example: a method based on harmonic wavelets and a technique involving deep learning neural networks. The study deals with the classification of hydroacoustic signals using coefficients of the harmonic wavelet transform (fast computation), short-time Fourier transform (spectrogram) and Fourier transform using a kNN-algorithm. Classification quality metrics (precision, recall and accuracy) are given for different signal-to-noise ratios. ROC curves were also obtained. The use of the deep neural network for classification of whales’ sounds is considered. The effectiveness of using harmonic wavelets for the classification of complex non-stationary signals is proved. A technique to reduce the feature space dimension using a ‘modulo N reduction’ method is proposed. A classification of 26 individual whales from the Whale FM Project dataset is presented. It is shown that the deep-learning-based approach provides the best result for the Whale FM Project dataset both for whale types and individuals.

The Gearbox Fault Detection of Machine Center by Using Time Frequency Order Spectrum

2012 ◽  
Vol 452-453 ◽  
pp. 1329-1333 ◽  
Author(s):  
C.C. Wang ◽  
Y. Kang ◽  
Y.L. Chung
Keyword(s):  
Fault Detection ◽  
Back Propagation ◽  
Machine Center ◽  
Rotor Systems ◽  
Time Frequency ◽  
Order Spectrum ◽  
Signal Features ◽  
Stationary Signals ◽  
Short Time ◽  
Frequency Features

Previously, for the case of fixed or steady state rotation rate, spectrum analysis can be used to extract the frequency features as the basis for the gearbox fault detection of machine center. However, the gearbox of machine center for increasingly instant speed variations mostly generate non-stationary signals, and the signal features must be averaged with analysis time which makes it difficult to identify the causes of failures. This study proposes a time frequency order spectrum method combining the short-time Fourier transform (STFT) and speed frequency order method to capture the order features of non-stationary signals. Such signal features do not change with speed, and are thus effective in identifying faults in mechanical components under non-stationary conditions. In this study, back propagation neural networks (BPNN) and time frequency order spectrum methods were used to verify faults diagnosis and obtained superior diagnosis results in non-stationary signals of gear-rotor systems in machine center.

scholarly journals A Study of Parameters Setting of the STADZT

10.14311/1654 ◽  
2012 ◽  
Vol 52 (5) ◽  
Author(s):  
Václav Turoň
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Frequency Resolution ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Stationary Signals ◽  
Zolotarev Polynomials ◽  
Non Stationary Signal ◽  
Short Time ◽  
Main Influence

This paper deals with the new time-frequency Short-Time Approximated Discrete Zolotarev Transform (STADZT), which is based on symmetrical Zolotarev polynomials. Due to the special properties of these polynomials, STADZT can be used for spectral analysis of stationary and non-stationary signals with the better time and frequency resolution than the widely used Short-Time Fourier Transform (STFT). This paper describes the parameters of STADZT that have the main influence on its properties and behaviour. The selected parameters include the shape and length of the segmentation window, and the segmentation overlap. Because STADZT is very similar to STFT, the paper includes a comparison of the spectral analysis of a non-stationary signal created by STADZT and by STFT with various settings of the parameters.

REDUNDANT MORPHOLOGICAL WAVELET AND LOCAL PATTERN SPECTRUM

2013 ◽  
Vol 11 (04) ◽  
pp. 1360001
Author(s):  
TOMONARI YAMAGUCHI ◽  
MITSUHIKO FUJIO ◽  
KATSUHIRO INOUE
Keyword(s):  
Wavelet Analysis ◽  
Discrete Wavelet ◽  
Short Time Fourier Transform ◽  
Shape Information ◽  
Local Pattern ◽  
Pattern Spectrum ◽  
Time Frequency ◽  
Stationary Signals ◽  
Shift Invariance ◽  
Short Time

Time-frequency analysis methods such as wavelet analysis are applied to investigate characteristic from non-stationary signals. In this study, we proposed redundant morphological wavelet analysis that was a kind of nonlinear discrete wavelet and redundant wavelet. This method analyzes a transition of shape information from signals in detail since this method keeps property of shift invariance though information of decomposition includes redundancy. Local pattern spectrum which corresponds to nonlinear short time Fourier transform is derived from this nonlinear wavelet. The characteristics of these methods were confirmed by applying to simulation data and actual data.

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