scholarly journals Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation

2017 ◽  
Vol 11 (5) ◽  
pp. 631-636 ◽  
Author(s):  
Wenderson Nascimento Lopes ◽  
Fabio Isaac Ferreira ◽  
Felipe Aparecido Alexandre ◽  
Danilo Marcus Santos Ribeiro ◽  
Pedro de Oliveira Conceição Junior ◽  
...  
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Digital Signal Processing ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Single Point ◽  
Digital Signal ◽  
Power Spectral

Resolving the Issues of Capon and APES Approach for Projecting Enhanced Spectral Estimation

2016 ◽  
Vol 6 (2) ◽  
pp. 725
Author(s):  
Kantipudi MVV Prasad ◽  
H.N. Suresh
Keyword(s):  
Signal Processing ◽  
Spectral Analysis ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Simple Technique ◽  
Spectral Estimation ◽  
Research Work ◽  
Research Community ◽  
The Past ◽  
Power Spectral

There are various applications on signal processing that is highly dependent on preciseness and accuracy of the outcomes in spectrum of signals. Hence, from the past two decades the research community has recognized the benefits, significance, as well as associated problems in carrying out a model for spectral estimation. While in-depth investigation of the existing literatures shows that there are various attempts by the researchers to solve the issues associated with spectral estimations, where majority of teh research work is inclined towards addressing problems associated with Capon and APES techniques of spectral analysis. Therefore, this paper introduces a very simple technique towards resolving the issues of Capon and APES techniques. The outcome of the study was analyzed using correlational factor and power spectral density to find the proposed system offers better spectral estimations compared to existing system.

Flank Wear Estimation in Turning Through Wavelet Representation of Acoustic Emission Signals

1997 ◽  
Vol 122 (1) ◽  
pp. 12-19 ◽  
Author(s):  
S. V. Kamarthi ◽  
S. R. T. Kumara ◽  
P. H. Cohen
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Fourier Transform ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Flank Wear ◽  
Wavelet Representation ◽  
Power Spectral ◽  
Frequency Components ◽  
Ae Signals

This paper investigates a flank wear estimation technique in turning through wavelet representation of acoustic emission (AE) signals. It is known that the power spectral density of AE signals in turning is sensitive to gradually increasing flank wear. In previous methods, the power spectral density of AE signals is computed from Fourier transform based techniques. To overcome some of the limitations associated with the Fourier representation of AE signals for flank wear estimation, wavelet representation of AE signals is investigated. This investigation is motivated by the superiority of the wavelet transform over the Fourier transform in analyzing rapidly changing signals such as AE, in which high frequency components are to be studied with sharper time resolution than low frequency components. The effectiveness of the wavelet representation of AE signals for flank wear estimation is investigated by conducting a set of turning experiments on AISI 6150 steel workpiece and K68 (C2) grade uncoated carbide inserts. In these experiments, flank wear is monitored through AE signals. A recurrent neural network of simple architecture is used to relate AE features to flank wear. Using this technique, accurate flank wear estimation results are obtained for the operating conditions that are within in the range of those used during neural network training. These results compared to those of Fourier transform representation are much superior. These findings indicate that the wavelet representation of AE signals is more effective in extracting the AE features sensitive to gradually increasing flank wear than the Fourier representation. [S1087-1357(00)71401-8]

scholarly journals Tool Condition Monitoring of Single-point Dressing Operation by Digital Signal Processing of AE and AI

Procedia CIRP ◽  
2018 ◽  
Vol 67 ◽  
pp. 307-312 ◽  
Author(s):  
Doriana M. D’Addona ◽  
Salvatore Conte ◽  
Wenderson Nascimento Lopes ◽  
Paulo R. de Aguiar ◽  
Eduardo C. Bianchi ◽  
...  
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Condition Monitoring ◽  
Single Point ◽  
Digital Signal ◽  
Tool Condition Monitoring ◽  
Tool Condition

Bayesian identification of bolted-joint parameters using measured power spectral density

2019 ◽  
Vol 234 (2) ◽  
pp. 260-274
Author(s):  
Yong Zhang ◽  
Yan Zhao ◽  
Yunyun Lu ◽  
Huajiang Ouyang
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Single Point ◽  
Identification Accuracy ◽  
Bayesian Probability ◽  
Conditional Probability Density ◽  
Bolted Joint ◽  
Power Spectral ◽  
Joint Parameters ◽  
Measured Power

A Bayesian method for the optimal estimation of parameters that characterize a bolted joint based on measured power spectral density is proposed in this article. Due to uncertainties such as measurement noise and modelling errors, it is difficult to identify joint parameters of a bolted structure accurately with incomplete measured response data. In this article, using the Bayesian probability framework to describe the uncertainty of the joint parameters and using the power spectrum of the structural response of the single-point/multi-point excitation as measurements, the conditional probability density function of the joint parameters is established. Then, the Bayesian maximum posterior estimation is performed by an optimization method. Two simplified bolted-joint models are built in the numerical examples. First, the feasibility of the proposed method in the undamped model is proved. Then, taking advantage of multi-point excitation, the identification accuracy of the proposed method in the damped model is improved. The numerical results show that the proposed method can accurately identify the stiffness and damping characteristics of joint parameters with good robustness to noise. Finally, the joint parameters of the finite element model for an aero-engine casing are identified by the proposed method with satisfactory accuracy.

scholarly journals Comparison of Semi-Empirical Single Point Wall Pressure Spectrum Models with Experimental Data

Fluids ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 270
Author(s):  
Nicholas Thomson ◽  
Joana Rocha
Keyword(s):  
Wind Tunnel ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Mean Squared Error ◽  
Single Point ◽  
Flight Test ◽  
Wall Pressure ◽  
Squared Error ◽  
Power Spectral ◽  
Semi Empirical

This study presents an evaluation of semi-empirical single-point wall pressure spectrum models by comparing model predictions with wind tunnel and flight test data. The mean squared error was used to compare the power spectral density of the wall pressure fluctuations predicted by semi-empirical models with a large amount of experimental data. Results show that the models proposed by Goody and Smol’yakov have the lowest mean squared error when predicting the power spectral density for wind tunnel experiments and the Rackl and Weston model has the lowest mean squared error when predicting the power spectral density for flight test data. In addition, although current studies of the power spectra obtained in the wind tunnel are similar, they are not generally an accurate representation of flight test experiments.

Computerised Signal Processing Systems

1986 ◽  
Vol 23 (3) ◽  
pp. 223-230
Author(s):  
R. Armstrong ◽  
B. Livingstone
Keyword(s):  
Signal Processing ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Spectral Resolution ◽  
Processing System ◽  
Educational Tool ◽  
Signal Processing System ◽  
Storage Oscilloscope ◽  
Auto Correlation ◽  
Power Spectral

The article describes a computerised signal processing system using a storage oscilloscope and a minicomputer, linked by an IEEE bus, which can be used both as a practical and educational tool. Results demonstrate F.F.T., spectral resolution, auto-correlation and power spectral density. A copy of the programme can be made available.

scholarly journals Digital Signal Processing for Acoustic Emission

10.5772/48557 ◽  
2012 ◽  
Author(s):  
Paulo R. ◽  
Cesar H.R. ◽  
Marcelo Marchi ◽  
Eduardo C.
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Digital Signal Processing ◽  
Digital Signal
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