Time-varying Wiener filtering of the fetal ECG using the wavelet transform

1995 ◽  
Author(s):  
M.S. Woolfson
Keyword(s):  
Wavelet Transform ◽  
Wiener Filtering ◽  
Time Varying ◽  
Fetal Ecg

Fetal ECG extraction from time-varying and low-rank noninvasive maternal abdominal recordings

2018 ◽  
Vol 39 (12) ◽  
pp. 125008 ◽  
Author(s):  
Fahimeh Jamshidian-Tehrani ◽  
Reza Sameni
Keyword(s):  
Low Rank ◽  
Time Varying ◽  
Fetal Ecg

scholarly journals Solution of a time-varying Wiener filtering problem

1967 ◽  
Vol 3 (12) ◽  
pp. 562 ◽  
Author(s):  
B.D.O. Anderson ◽  
J.B. Moore
Keyword(s):  
Wiener Filtering ◽  
Time Varying ◽  
Filtering Problem

Bearing fault detection under time-varying speed based on empirical wavelet transform, cultural clan-based optimization algorithm, and random forest classifier

2021 ◽  
pp. 107754632110470
Author(s):  
Moussaoui Imane ◽  
Chemseddine Rahmoune ◽  
Mohamed Zair ◽  
Djamel Benazzouz
Keyword(s):  
Wavelet Transform ◽  
Random Forest ◽  
Fault Detection ◽  
Optimization Algorithm ◽  
High Efficiency ◽  
Time Varying ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Empirical Wavelet Transform ◽  
Bearing Condition Monitoring

Bearings are massively utilized in industries of nowadays due to their huge importance. Nevertheless, their defects can heavily affect the machines performance. Therefore, many researchers are working on bearing fault detection and classification; however, most of the works are carried out under constant speed conditions, while bearings usually operate under varying speed conditions making the task more challenging. In this paper, we propose a new method for bearing condition monitoring under time-varying speed that is able to detect the fault efficiently from the vibration signatures. First, the vibration signal is processed with the Empirical Wavelet Transform to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then, the features’ set is reduced using the Cultural Clan-based optimization algorithm by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm “Random Forest” is used to train a model able to classify the fault based on the selected features. The proposed method was tested on a time-varying real dataset consisting of three different bearing health states: healthy, outer race defect, and inner race defect. The obtained results indicate the ability of our proposed method to handle the speed variability issue in bearing fault detection with high efficiency.

scholarly journals Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170254 ◽  
Author(s):  
Jean Baptiste Tary ◽  
Roberto Henry Herrera ◽  
Mirko van der Baan
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Signal ◽  
American Football ◽  
Frequency Resolution ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Frequency Information ◽  
Synchrosqueezing Transform

The continuous wavelet transform (CWT) has played a key role in the analysis of time-frequency information in many different fields of science and engineering. It builds on the classical short-time Fourier transform but allows for variable time-frequency resolution. Yet, interpretation of the resulting spectral decomposition is often hindered by smearing and leakage of individual frequency components. Computation of instantaneous frequencies, combined by frequency reassignment, may then be applied by highly localized techniques, such as the synchrosqueezing transform and ConceFT, in order to reduce these effects. In this paper, we present the synchrosqueezing transform together with the CWT and illustrate their relative performances using four signals from different fields, namely the LIGO signal showing gravitational waves, a ‘FanQuake’ signal displaying observed vibrations during an American football game, a seismic recording of the M w 8.2 Chiapas earthquake, Mexico, of 8 September 2017, followed by the Irma hurricane, and a volcano-seismic signal recorded at the Popocatépetl volcano showing a tremor followed by harmonic resonances. These examples illustrate how high-localization techniques improve analysis of the time-frequency information of time-varying signals. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

A Multi-Resolution PID Controller Based on the Wavelet Transform

2012 ◽  
Vol 472-475 ◽  
pp. 632-636 ◽  
Author(s):  
Cun Zhi Yao ◽  
Gui Xiang Zhang
Keyword(s):  
Control System ◽  
Wavelet Transform ◽  
Pid Controller ◽  
Control Design ◽  
Cumulative Effect ◽  
Measurement Noise ◽  
Time Varying ◽  
Error Signal ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller

The non-linear and time-varying natures of the process together with the large disturbances of several types are the key challenge for the control design. A controller based on multi- resolution decomposition using wavelets is presented in the paper. The wavelet is used to decompose the error signal into signals at different scales.These signals are then used to compensate for the uncertainties in the plant.The controller is similar to proportional integral derivative controller in principle and application. the output from this control system represents the cumulative effect of uncertainties such as measurement noise, frictional variations and external torque disturbances which manifest at different scales. This controller better solves the nonlinear and time-varying togetther with the great disturbance.

Using a Modified Harmonic Wavelet Transform to Characterize Mechanical Shock

2011 ◽  
Vol 54 (2) ◽  
pp. 85-102
Author(s):  
David Smallwood
Keyword(s):  
Wavelet Transform ◽  
Spectral Density ◽  
Estimation Error ◽  
Cross Product ◽  
Constant Time ◽  
Time Varying ◽  
Mechanical Shock ◽  
Time Frequency ◽  
Harmonic Wavelet

A modified harmonic wavelet transform is used to estimate a time varying spectral density. The resolution of the estimate has an approximate constant time-frequency product. The estimation error is directly related to this time-frequency product. Unwanted cross product terms are effectively minimized. Several examples are given: White random, two sine waves, chirps, impulses, sums of exponentially decaying sinusoids, and a pyroshock. It is also shown how realizations can be generated from the modified harmonic wavelet transform estimate of the time varying spectral density.

Structural time-varying damage detection using synchrosqueezing wavelet transform

2015 ◽  
Vol 15 (1) ◽  
pp. 119-133 ◽  
Author(s):  
Jing-Liang Liu ◽  
Zuo-Cai Wang ◽  
Wei-Xin Ren ◽  
Xing-Xin Li
Keyword(s):  
Wavelet Transform ◽  
Damage Detection ◽  
Time Varying ◽  
Structural Time
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