Analysis of Noise-Like Signals of Acoustic Emission Methods Broadband Filtering

Растегаев, И. А.; Myhre, Aleksander Omholt; Vinogradov, Alexei; Мерсон, Д. Л.; Rastegaeva, I. I.

doi:10.14489/td.2014.08.pp.049-056

Cited by 3 publications

(2 citation statements)

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“…Several filter sub-variants were implemented with different assumptions regarding the reasons of noise and signal distortion [45][46][47][48]. These assumptions were used to choose the S n (f) and C(f) functions for F1 and F2 filters, respectively F1.…”

Section: Ae Data Acquisition and Processingmentioning

confidence: 99%

A Time-Frequency Based Approach for Acoustic Emission Assessment of Sliding Wear

et al. 2020

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The acoustic emission method is one of few contemporary non-destructive testing techniques enabling continuous on-line health monitoring and control of tribological systems. However, the existence of multiple “pseudo”-acoustic emission (AE) and noise sources during friction, and their random occurrence poses serious challenges for researchers and practitioners when extracting “useful” information from the upcoming AE signal. These challenges and numerous uncertainties in signal classification prevent the unequivocal interpretation of results and hinder wider uptake of the AE technique despite its apparent advantages. Currently, the signal recording and processing technologies are booming, and new applications are born on this support. Specific tribology applications, therefore, call for developing new and tuning existing approaches to the online AE monitoring and analysis. In the present work, we critically analyze, compare and summarize the results of the application of several filtering techniques and AE signal classifiers in model tribological sliding friction systems allowing for the simulation of predominant wear mechanisms. Several effective schemes of AE data processing were identified through extensive comparative studies. Guidelines were provided for practical application, including the online monitoring and control of the systems with friction, characterizing the severity and timing of damage, on-line evaluation of wear as sliding contact tests and instrumented acceleration of tribological testing and cost reduction.

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Section: Ae Data Acquisition and Processingmentioning

confidence: 99%

A Time-Frequency Based Approach for Acoustic Emission Assessment of Sliding Wear

et al. 2020

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“…1). В методе акустической эмиссии (АЭ) периодограммы используются при: вычислении энергии (мощности) сигналов [1], полосовой частотной фильтрации или широкополосном шумопонижении [2][3][4], кластеризации данных и распознавании источников АЭ [5][6][7], оценке передаточных характеристик акустического тракта и затухания в нем [8], гармоническом анализе работы узлов циклического действия [9] и т. д. Однако периодограмма не позволяет обнаружить, в какой момент времени возникают те или иные отличия в частотной области, т. е. не отображает динамику частотных изменений в сигнале (рис. 1).…”

Section: Introductionunclassified

The Comparison of the Main Time-Frequency Transformations of Spectral Analysis of Acoustic Emission Signals

Rastegaeva¹,

Растегаев²,

Agletdinov³

et al. 2022

FMT

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Due to the intensive development of spectroscopic techniques for detecting acoustic emission signals, the problem of providing the best time-frequency resolution through the application of specific time-frequency transformation algorithms comes to the fore. The Short-Time Fourier Transform, the Wavelet Transform, the Smoothed Pseudo Wigner Distribution, the Choi-Williams Distribution, and the Hilbert-Huang Transform are currently the main time-frequency transformations used or integrated into the acoustic emission method. However, today in the literature, there is not enough information that allows evaluating time-frequency transformations regarding the effectiveness of their application to specify the features of discrete and continuous acoustic emission signals. On this basis, the authors carried out an experimental comparison of synthetic and actual model signals to determine the efficiency of specified time-frequency transformations. The synthetic model signals were a chirp signal, ideal sinusoids, and a Dirac delta function. The actual signals were a discrete acoustic emission signal from the Hsu Nelson source decomposed into dispersion modes in the acoustic channel and a continuous acoustic emission signal from the air outflow through a calibrated hole. The analysis shows that only the Fourier transform and the Wavelet transform can define all control features of model signals at the frequency components’ energy gap of about 25 dB. Wigner Distribution, Choi-Williams Distribution, and Hilbert-Huang Transform demonstrated higher time-frequency resolution did not identify frequency components of low energy. Therefore, the authors recommend using them to identify spectral changes in the resonance and discrete signals but in the narrow energy range. The Fourier transform and the Wavelet transform demonstrated the best result to analyze continuous acoustic emission. However, to use the latter, the procedure of selection of the optimal basis function is necessary. The study determined that the Hilbert-Huang transform allows identifying the frequency fluctuations, but it is necessary to develop ways to increase sensitivity and extract basic information from the spectrograms to enhance the validity of its results.

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Structural, thermal and acoustic aspects of crack propagation in titanium alloys

Iziumova,

Vshivkov,

Panteleev

et al. 2024

Engineering Fracture Mechanics

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Analysis of Noise-Like Signals of Acoustic Emission Methods Broadband Filtering

Cited by 3 publications

References 0 publications

A Time-Frequency Based Approach for Acoustic Emission Assessment of Sliding Wear

A Time-Frequency Based Approach for Acoustic Emission Assessment of Sliding Wear

The Comparison of the Main Time-Frequency Transformations of Spectral Analysis of Acoustic Emission Signals

Structural, thermal and acoustic aspects of crack propagation in titanium alloys

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