A Help for Lifetime Management: Defect Characterisation Through Signal Processing of Ultrasonic Data

2003 ◽  
Author(s):  
S. Gautier ◽  
L. Cha˜tellier
Keyword(s):  
Signal Processing ◽  
Real Data ◽  
Data Sets ◽  
Height Estimation ◽  
Planar Defects ◽  
Mechanical Constraints ◽  
Processing Step ◽  
Non Destructive ◽  
Defect Characterisation

So as to detect and to characterize potential defects on cast components, a program of in-service inspections is carried out for the non destructive evaluation (NDE) of the component. A first step of inspection usually consists in detection. Still, should a defect be detected, one can be asked to prove the component still stands the mechanical constraints. Then, it is very important to be able to characterize precisely the size and the position of the defect. In the studied case, we concentrate on planar defects which are to be characterized from ultrasonic. The estimation of the height consists in measuring the time of flight between two reflectors: the diffraction and the corner echoes. But in case of small defects, the two echoes overlap and it is very difficult to give a height estimation of the defect. Then, the core of our contribution deals with the signal processing method we developed for the estimation of the height of planar defects. The use of our so-called deconvolution process now enables to obtain the height estimation of the defect, when it was impossible without processing. Processing results of real data sets are given for the inspection of representative mock-ups of homogeneous austenitic welds. In conclusion, the signal processing step turns out to be an important step in the characterization of defects and can thus contributes to maintain some components in service.

scholarly journals Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs

2021 ◽  
Author(s):  
Karin Schork ◽  
Michael Turewicz ◽  
Julian Uszkoreit ◽  
Jörg Rahnenführer ◽  
Martin Eisenacher
Keyword(s):  
Real Data ◽  
Bipartite Graphs ◽  
Data Sets ◽  
Complex Structures ◽  
Protein Databases ◽  
Protein Inference ◽  
The Relationship ◽  
Ambiguity Groups ◽  
Proteins And Peptides

Motivation: In bottom-up proteomics, proteins are enzymatically digested before measurement with mass spectrometry. The relationship between proteins and peptides can be represented by bipartite graphs. This representation is useful to aid protein inference and quantification, which is complex due to the occurrence of shared peptides. We conducted a comprehensive analysis of bipartite graphs using theoretical peptides from in silico digestion of protein databases as well as quantified peptides quantified from real data sets. Results: The graphs based on quantified peptides are smaller and have less complex structures compared to graphs using theoretical peptides. The proportion of protein nodes without unique peptides and of graphs that contain such proteins are considerably greater for real data. Large differences between the two analyzed organisms (mouse and yeast) on database as well as quantitative level have been observed. Insights of this analysis may be useful for the development of protein inference and quantification algorithms.

Demonstration of Improved Quantitative Mobility Spectrum Analysis (i-QMSA)

1997 ◽  
Vol 490 ◽  
Author(s):  
I. Vurgaftman ◽  
J. R. Meyer ◽  
C. A. Hoffman ◽  
D. Redfern ◽  
J. Antoszewski ◽  
...  
Keyword(s):  
Spectrum Analysis ◽  
Real Data ◽  
Semiconductor Materials ◽  
Data Sets ◽  
Mobility Spectrum ◽  
Data Set ◽  
Standard Tool ◽  
Field Dependent ◽  
Quantitative Mobility Spectrum Analysis

ABSTRACTWe discuss an improved quantitative mobility spectrum analysis (i-QMSA) of magnetic-field-dependent Hall and resistivity data, which can determine multiple electron and hole densities and mobilities. A fully automated computer implementation of i-QMSA is applied to a variety of synthetic and real data sets. The results show that the new algorithm increases the information available from a given data set and is suitable for use as a standard tool in the characterization of semiconductor materials and devices.

Algorithm Study for Metal Magnetic Memory Signal Processing

2012 ◽  
Author(s):  
Zhanjun Feng ◽  
Weibin Wang ◽  
Hongjun Dong ◽  
Song Lin ◽  
Dianxue Wang ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Data ◽  
Experimental Result ◽  
Work Piece ◽  
Magnetic Memory ◽  
Metal Magnetic Memory ◽  
Destructive Testing ◽  
Singularity Detection ◽  
Signal Processing Methods ◽  
Non Destructive

Currently, Metal Magnetic Memory (MMM) non-destructive testing has been applied on trenchless inspection for buried pipeline. However, the problem of signal pattern recognition still exists. This paper introduces two signal processing methods to enhance the inspection accuracy of MMM: the signal segmented fluctuation and the signal segmented dissimilarity. Both of the two methods are designed for Signal singularity detection (SSD) of MMM signal. It is well known that the MMM signal of the stress concentrated area (SCA) is more fluctuant and dissimilar in contrast to those of non SCA, which is the basis of deriving the two algorithms. The two methods have the advantage in the detection of the metallic work piece which is non defective in looks but fatigued as well as may be helpful to detect the singularity of the signals such as negative pressure wave, ultrasonic wave etc. Experimental result with real data demonstrates the effectiveness of the proposed algorithms. Moreover, the MMM SSD software implementation is considered.

Topography and displacement of polar glaciers from multi-temporal SAR interferograms

Polar Record ◽  
2007 ◽  
Vol 43 (4) ◽  
pp. 331-343 ◽  
Author(s):  
Franz J. Meyer
Keyword(s):  
Real Data ◽  
Data Sets ◽  
Temporal Data ◽  
Error Sources ◽  
Processing Step ◽  
Multi Temporal ◽  
Least Squares Adjustment ◽  
Temporal Model ◽  
Spatio Temporal ◽  
A New Technique

ABSTRACTThis paper describes a new technique simultaneously to estimate topography and motion of polar glaciers from multi-temporal SAR interferograms. The approach is based on a combination of several SAR interferograms in a least-squares adjustment using the Gauss-Markov model. For connecting the multi-temporal data sets, a spatio-temporal model is proposed that describes the properties of the surface and its temporal evolution. Rigorous mathematical modelling of functional and stochastic relations allows for a systematic description of the processing chain. It is also an optimal tool to set parameters for the statistics of every individual processing step, and the propagation of errors into the results. Within the paper theoretical standard deviations of the unknowns are calculated depending on the configuration of the data sets. The influence of gross errors in the observations and the effect of non-modelled error sources on the unknowns are estimated. A validation of the approach based on real data concludes the paper.

Taking Advantage of Signal Processing for Lifetime Management

2002 ◽  
Author(s):  
S. Gautier ◽  
L. Chaˆtellier
Keyword(s):  
Signal Processing ◽  
Narrow Beam ◽  
X Ray ◽  
Essential Step ◽  
Mechanical Constraints ◽  
X Ray Imaging

So as to detect and characterize potential defects in pipes, inspections are carried out. Should a defect be detected, one can be asked to prove the component still stands the mechanical constraints. Then, it is essential to characterize the defect, especially when the demonstration relies on mechanical calculus: the better the characterization, the better the mechanical assessment. Still, the characterization of the defect from the sole data is sometimes very difficult and the justification process can thus be dramatically jeopardized. In such cases, signal processing can be very helpful for the interpretation of the data and for the defect’s characterization. This paper is dedicated to the inspection of pipes with narrow beam X-ray imaging. For this application, the characterization of the defect is indeed very difficult. We show that the signal processing makes it possible to achieve a 3-D reconstruction of the component providing a characterization of the defects. Thus, the signal processing can turn out to be an essential step between measurement and mechanical calculus.

Non Destructive Electrical Defect Characterisation and Topography of Silicon Wafers and Epitaxial Layers

2005 ◽  
Vol 864 ◽  
Author(s):  
K. Dornich ◽  
T. Hahn ◽  
J.R. Niklas
Keyword(s):  
Diffusion Length ◽  
High Sensitivity ◽  
Silicon Wafers ◽  
Charge Carriers ◽  
Epitaxial Layers ◽  
Thermal Excitation ◽  
Non Destructive ◽  
And Diffusion ◽  
Defect Characterisation

AbstractRecent progress in experimental technique made it possible to improve the sensitivity of microwave detected photoconductivity by several orders of magnitude. This opens completely new possibilities for a contact less non-destructive electrical defect characterization of silicon wafers and even of epitaxial layers on substrates with extremely low resistivity. Electrical properties such as lifetime, mobility and diffusion length can be measured without contacts also at very low injection levels with a resolution only limited by the diffusion length of the charge carriers. The doping level of the material plays no major role.Owing to the high sensitivity, thermal excitation of charge carriers out of defect levels filled during the photo pulse can also be observed. This leads to defect specific photoconductivity transients which deliver pieces of information like DLTS, however, again without contacts, non critical doping, and with high spatial resolution.

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