scholarly journals Online segmentation of acoustic emission data streams for detection of damages in composites structures in unconstrained environments

2014 ◽  
pp. 525-532 ◽  
Author(s):  
V Placet ◽  
E Ramasso ◽  
L Boubakar ◽  
N Zerhouni
Keyword(s):  
Acoustic Emission ◽  
Data Streams ◽  
Emission Data

Acoustic emission waveforms for damage monitoring in composite materials: Shifting in spectral density, entropy and wavelet packet transform

2021 ◽  
pp. 147592172110446
Author(s):  
Claudia Barile ◽  
Caterina Casavola ◽  
Giovanni Pappalettera ◽  
Vimalathithan Paramsamy Kannan
Keyword(s):  
Acoustic Emission ◽  
Spectral Density ◽  
Flicker Noise ◽  
Wavelet Packet ◽  
Research Work ◽  
Wavelet Packet Transform ◽  
Spectral Energy ◽  
Emission Data ◽  
Cfrp Composites ◽  
Damage Process

Signal-based acoustic emission data are analysed in this research work for identifying the damage modes in carbon fibre–reinforced plastic (CFRP) composites. The research work is divided into three parts: analysis of the shifting in the spectral density of acoustic waveforms, use of waveform entropy for selecting the best wavelet and implementation of wavelet packet transform (WPT) for identifying the damage process. The first two methodologies introduced in this research work are novel. Shifting in the spectral density is introduced in analogous to ‘flicker noise’ which is popular in the field of waveform processing. The entropy-based wavelet selection is refined by using quadratic Renyi’s entropy and comparing the spectral energy of the dominating frequency band of the acoustic waveforms. Based on the method, ‘dmey’ wavelet is selected for analysing the waveforms using WPT. The slope values of the shifting in spectral density coincide with the results obtained from WPT in characterising the damage modes. The methodologies introduced in this research work are promising. They serve the purpose of identifying the damage process effectively in the CFRP composites.

The study of a fatigue crack propagation in titanium Grade 2 using analysis of energy dissipation and acoustic emission data

2019 ◽  
Vol 210 ◽  
pp. 312-319 ◽  
Author(s):  
A.N. Vshivkov ◽  
A. Yu. Iziumova ◽  
I.A. Panteleev ◽  
A.V. Ilinykh ◽  
V.E. Wildemann ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fatigue Crack ◽  
Energy Dissipation ◽  
Crack Propagation ◽  
Fatigue Crack Propagation ◽  
Emission Data ◽  
Titanium Grade

scholarly journals Novel evaluation method of acoustic emission data based on statistical fiber bundle cells

2019 ◽  
Vol 53 (17) ◽  
pp. 2429-2446
Author(s):  
László M Vas ◽  
Zoltán Kocsis ◽  
Tibor Czigány ◽  
Péter Tamás ◽  
Gábor Romhány
Keyword(s):  
Acoustic Emission ◽  
Fiber Bundle ◽  
Evaluation Method ◽  
Emission Data

scholarly journals Application of classification neural networks for identification of damage stages of degraded low alloy steel based on acoustic emission data analysis

2020 ◽  
Vol 20 (4) ◽  
Author(s):  
Joanna Krajewska-Śpiewak ◽  
Igor Lasota ◽  
Barbara Kozub
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Alloy Steel ◽  
Learning Algorithm ◽  
Structural Condition ◽  
Oil Refinery ◽  
Low Alloy Steel ◽  
Emission Data ◽  
Methodology Development ◽  
Static Tensile

Abstract The paper presents the influence of low alloy steel degradation on the acoustic emission (AE) generated during static tension of notched specimen. The material was cut from a technological pipeline long-term operated in the oil refinery industry. Comparative analysis of AE activity generated by damage process of degraded and new material has been carried out. The different AE parameters were used to detect different stages of fracture process of low alloy steel under quasi-static tensile test. Neural networks with three layers were created with Broyden–Fletcher–Goldfarb–Shanno learning algorithm for a database analysis. The different AE parameters were included in the input layer. Classification neural networks were created in order to determine the stages of material degradation. The results obtained from the carried out studies will be used as the basis for new methodology development of the assessment of the structural condition of in-service equipment.

Some Aspects of Cr–Mo–V Rotor Steel Damage due to Long-Term Service from Acoustic Emission Data

2018 ◽  
Vol 63 (12) ◽  
pp. 1840-1844 ◽  
Author(s):  
A. M. Leksovskii ◽  
S. N. Isaev ◽  
B. L. Baskin
Keyword(s):  
Acoustic Emission ◽  
Rotor Steel ◽  
Emission Data

A novel Bayesian approach to acoustic emission data analysis

Ultrasonics ◽  
2016 ◽  
Vol 72 ◽  
pp. 89-94 ◽  
Author(s):  
E. Agletdinov ◽  
E. Pomponi ◽  
D. Merson ◽  
A. Vinogradov
Keyword(s):  
Acoustic Emission ◽  
Data Analysis ◽  
Bayesian Approach ◽  
Emission Data

Design of Acoustic Emission Data Acquisition System of Wood Damage

2015 ◽  
Vol 8 (9) ◽  
pp. 291-302 ◽  
Author(s):  
Junmei Zhang ◽  
Yanyi Wang ◽  
Bowen Cheng ◽  
Shuai Wang
Keyword(s):  
Acoustic Emission ◽  
Data Acquisition ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Emission Data
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