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

scholarly journals The Identification of the Deformation Stage of a Metal Specimen Based on Acoustic Emission Data Analysis

Sensors ◽  
2017 ◽  
Vol 17 (4) ◽  
pp. 789 ◽  
Author(s):  
Shenao Zou ◽  
Fengying Yan ◽  
Guoan Yang ◽  
Wei Sun
Keyword(s):  
Acoustic Emission ◽  
Data Analysis ◽  
Emission Data ◽  
Deformation Stage ◽  
Metal Specimen

scholarly journals Acoustic Emission Data Analysis of Tank Settlement

2021 ◽  
Vol 2025 (1) ◽  
pp. 012074
Author(s):  
Guotao Fan ◽  
Yongtao Zhao
Keyword(s):  
Acoustic Emission ◽  
Data Analysis ◽  
Emission Data

Automated Emission Data Registration and Segmentation for IC Analysis

2016 ◽  
Author(s):  
Franco Stellari ◽  
Peilin Song
Keyword(s):  
Data Analysis ◽  
Registration Accuracy ◽  
Emission Data ◽  
Data Registration ◽  
Different Types ◽  
Test Chips

Abstract In this paper, the development of advanced emission data analysis methodologies for IC debugging and characterization is discussed. Techniques for automated layout to emission registration and data segmentations are proposed and demonstrated using both 22 nm and 14 nm SOI test chips. In particular, gate level registration accuracy is leveraged to compare the emission of different types of gates and quickly create variability maps automatically.

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.

Sign in / Sign up

Export Citation Format

Share Document