scholarly journals Filter Realization of the Time-Domain Average Denoising Method for a Mechanical Signal

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wuwei Feng ◽  
Cuizhu Wang ◽  
Xin Chen ◽  
Yuzhou Shi ◽  
Meng Jiang ◽  
...  
Keyword(s):  
Time Domain ◽  
Large Error ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Denoising Method ◽  
Practical Applications ◽  
Noise Interference ◽  
Mechanical Signal ◽  
The Time Domain ◽  
Periodic Components

Time-domain averaging (TDA) is an effective signal processing technique in fault diagnosis that can extract the periodic components of interest from signals mixed with noise interference while suppressing other irrelevant periodic signals. However, there are two obvious shortcomings to TDA: first, the acquisition of keyphasor signals is often restricted by the application environment and conditions. Even if the signal is obtained by TDA, owing to the existence of periodic truncation errors, satisfactory results cannot be obtained. Second, due to the velocity fluctuation, the actual mechanical signal is easy to produce a large error in TDA stacking. To solve the above challenges, first, based on the disadvantage of using traditional resampling to solve the TDA synchronization problem, this paper proposes a new method of subsection resampling, which improves the analysis effect of the traditional TDA. Second, to further expand the range of the practical applications, according to the amplitude-frequency map of TDA, a method for realizing TDA function by using the FIR multiband filter is proposed. This approach effectively avoids the requirement of traditional methods to collect the keyphasor signals and broadens the application in practical engineering. Finally, the improved TDA method is compared with the filter implementation technology, and their respective application conditions are given.

The Signal Processing Technique in the Time-Domain Approach of Determining the Residual Error Terms of the Calibrated VNA

2013 ◽  
Vol 333-335 ◽  
pp. 707-710 ◽  
Author(s):  
Cui Lian Song ◽  
Xin Meng Liu ◽  
Hui Huang
Keyword(s):  
Time Domain ◽  
Linear Prediction ◽  
Inverse Fourier Transform ◽  
Processing Technique ◽  
Residual Error ◽  
Measured Signal ◽  
Improved Method ◽  
Signal Processing Technique ◽  
Error Terms ◽  
The Time Domain

Sophisticate mathematic computation is used when the residual error parameters are estimated employing time domain technique. This paper discuss the improved method of employing Window function before the inverse Fourier transform. At the same time, we find the curve of time domain will be out of the standard shape when the linear prediction of the measured signal section is too long, and some figures are offered to illustrate it.

scholarly journals Signal Processing Technique for Time Domain Measurement of S-Parameters

2016 ◽  
Vol 15 (2) ◽  
pp. 105-114 ◽  
Author(s):  
Isabela M. Nobre ◽  
Julio L. Nicolini ◽  
Joaquim D. Garcia ◽  
Marbey Mosso
Keyword(s):  
Signal Processing ◽  
Time Domain ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
S Parameters

Dip moveout in the Radon domain

Geophysics ◽  
1999 ◽  
Vol 64 (1) ◽  
pp. 278-288
Author(s):  
Chengshu Wang
Keyword(s):  
Time Domain ◽  
Three Dimensional ◽  
Fourier Method ◽  
Real Data ◽  
Processing Technique ◽  
The Common ◽  
The Time Domain

I consider a new dip‐moveout (DMO) processing technique in the Radon domain called Radon DMO. The Radon DMO operator directly maps data from the NMO-corrected time domain to the DMO wavefield in the Radon domain. The method is built upon a process that transforms a single NMO-corrected trace into multiple traces spread along hyperbolas in the Radon domain. These hyperbolas are a linear Radon map of the DMO ellipses in the time domain. In this paper, I introduce the amplitude‐preserving Radon DMO and compare some examples of Radon DMO and Fourier DMO for both synthetic and real data. I also show the better frequency preservation properties of the Radon DMO method. Three‐dimensional data are often irregularly sampled with respect to fold, azimuth, and offset. Population deficiencies are exaggerated in the common‐offset domain. Radon DMO does not require that input traces belong to one common‐offset bin as does the Fourier method. Input traces can be organized from multiple offset bins grouping to perform Radon DMO, which is well used in 3-D surveys. Some synthetic and real data examples show these properties.

Intelligent Household Application Base on Mobile Phone the Ultrasonic Nondestructive Testing Technology Based on the Signal Processing Technique

2012 ◽  
Vol 155-156 ◽  
pp. 470-473
Author(s):  
Shu Peng Wei
Keyword(s):  
Signal Processing ◽  
Time Domain ◽  
Measurement Techniques ◽  
Processing Technique ◽  
Ultrasonic Nondestructive Testing ◽  
Excellent Performance ◽  
Signal Processing Technique ◽  
Testing Technology ◽  
Small Cracks ◽  
Strong Scattering

The ultrasonic nondestructive detection measurement techniques is so successful, it not only based on production and income measures of the wave, but also to the basis and the shrinkage of the waveform of get signal processing. Although the traditional time domain method can successfully sure small cracks, but you can't estimate the size of the crack, especially in the strong scattering noise to the influence of the after. Experimental results show that this algorithm not only has excellent performance, still can strong presence of noise in the signal processing, also can succeed estimate the size and location of the crack. In addition, this algorithm can be applied to ultrasonic nondestructive signal data compression.

scholarly journals Introduction to the method of estimating time-domain response based on amplitude-frequency characteristics

2021 ◽  
Vol 2087 (1) ◽  
pp. 012061
Author(s):  
Mingrui Wang ◽  
Mei Xu ◽  
Jiangfeng Wang ◽  
Yingying Guo
Keyword(s):  
Research And Development ◽  
Time Domain ◽  
Electromagnetic Pulse ◽  
Parametric Modeling ◽  
Frequency Characteristics ◽  
Practical Applications ◽  
Time Domain Response ◽  
The Time Domain ◽  
Non Parametric

Abstract How to use the amplitude-frequency characteristics to reconstruct the signal to obtain the time-domain response has always been a concern in the field of nuclear electromagnetic protection. So far, in practical applications, parametric modeling and non-parametric modeling have been used to solve related problems. This article summarizes the research and development of using amplitude-frequency characteristics to recover time-domain signals in the field of nuclear electromagnetic pulse protection, and briefly introduces the shortcomings of the two methods in combination with specific experiments.

Clutter removal for measuring low-frequency narrow-band antenna using spectral estimation

2018 ◽  
Vol 11 (3) ◽  
pp. 215-219
Author(s):  
C. F. Hu ◽  
N. J. Li
Keyword(s):  
Time Domain ◽  
Measurement Accuracy ◽  
Narrow Band ◽  
Spectral Estimation ◽  
Low Frequency ◽  
Ar Model ◽  
Signal Processing Technique ◽  
Antenna Measurement ◽  
The Time Domain ◽  
Time Gating

AbstractThe measurement accuracy of low-frequency narrow-band antenna is heavily influenced by its environment, which is also difficult to remove the clutter with a time gating. This paper proposes a method to improve the measurement accuracy of low-frequency narrow-band antenna using signal processing technique. The method is to predict the unknown value out of received original signal with an auto-regressive model (AR model) based on modern spectral estimation theory, and the parameters in AR model are calculated by maximum entropy spectral estimation algorithm. Thus, a wideband signal compared with the original band is obtained, and then the time-domain resolution is enhanced. The time gating is more exactly to separate the antenna radiation signal from multipath signals. The simulation and experimental results show that about 50% extended data for each ends of original band can be obtained after spectral extrapolation, and the time-domain resolution after extrapolation is twice than the original narrow-band signal, and the influence of measurement environment can be eliminated effectively. The method can be used to improve accuracy in actual antenna measurement.

scholarly journals Research of Harmonics in Power System Signal using Gaussian’s Distribution Overlapping by Receiver Operating Characteristics (Roc) Curve

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1087-1091
Keyword(s):  
Probability Distribution ◽  
Power System ◽  
Frequency Domain ◽  
Frequency Distribution ◽  
Time Domain ◽  
Processing Technique ◽  
Operating Characteristics ◽  
Interval Probability ◽  
The Time Domain ◽  
Even Harmonics

Harmonic analysis of the power system signal is a proliferating research in the field of electronics technology. Whenever, we analyze odd and ever harmonics are present in the signal, imperative operation is needed to transform from the time domain to the frequency domain. Hence, all the researchers are utilizing the Fourier Transform technique is very effective for the analysis of odd and even harmonics in the frequency domain. In the past two decades, Wavelet Transform is a wonderful technique to analyze the harmonics both frequency and time domain as well. The analysis of harmonic and its probability distribution are most important for the purpose to predict the harmonic effects in the present situation. We treated all the harmonics and its corresponding frequency distribution are considered as a zero mean unit variance. The overlapping these distributions (small, medium, large) are analyzed with help of statistical data processing technique. It is one of the most important basic plots in the decision theory and it provides the constructive decision about the overlapping of a frequency distribution in power system signal. The curvature as a plot of sensitivity and specificity underlying the harmonics are present and not present distributive (Gaussian). The above determined values are lying in the interval probability [0, 1] and it is depends only the nature of the dataset. In this paper, we explained with help of MATLAB and level of understand the basic concept of ROC is demonstrated. The dataset is drawn from the example of odd and even harmonics are generated and the probability distribution as input to our MATLAB program.

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