Fractal Analysis of Weld Defect Patterns Obtained by Radiographic Tests

Tesser, J. A.; Lopes, Rogério; Vieira, André P.; Gonçalves, L. L.; Rebello, J. M. A.

doi:10.1063/1.2718018

Cited by 4 publications

(3 citation statements)

References 9 publications

(17 reference statements)

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“…The results reported are averages taken over these 100 sets, and almost a 100% average success rate classification has been achieved for the training and testing sets in all classification schemes by combining the Hurst and DF analyses. The present results confirm previous ones [29][30][31], which show that the statistical fluctuation and fractal analysis combined with classification techniques for pattern recognition constitute a powerful tool when applied to ultrasonic measurements. Furthermore, in this case the technique was applied to backscattering signals obtained from pulse echo testing which the simplest ultrasonic nondestructive technique.…”

Section: Discussionsupporting

confidence: 90%

Characterization of Cast Iron Microstructure Through Fluctuation and Fractal Analyses of Ultrasonic Backscattered Signals Combined with Classification Techniques

et al. 2011

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This work aims at evaluating the performance of pattern recognition methods in the identification of different microstructures presented by cast iron, namely, lamellar, vermicular and nodular microstructures, through the statistical fluctuation and fractal analyses of backscattered ultrasonic signals. The signals were obtained with a broad band ultrasonic probe with a central frequency of 5 MHz. The statistical fluctuations of the ultrasonic signals were analyzed by means of Hurst (RSA) and detrended-fluctuation analyses (DFA), and the fractal analyses were carried out by applying the minimal cover and box-counting techniques to the signals. The curves obtained from the statistical fluctuations and fractal analyses, as functions of the time window, were processed by using four pattern classification techniques, namely, principal-component analysis (PCA), Karhunen-Loève transformation (KLT), neural networks and Gaussian classifier. The best results were obtained by Karhunen-Loève expansion and neural networks, where an approximately 100% success rate has been reached for the classification of the different microstructures as well as for the training and the testing sets of events. The results presented correspond to an average taken over 100 randomly chosen sets of events. These results indicate that, within the techniques used, the Karhunen-Loève transformation and

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Section: Discussionsupporting

confidence: 90%

Characterization of Cast Iron Microstructure Through Fluctuation and Fractal Analyses of Ultrasonic Backscattered Signals Combined with Classification Techniques

et al. 2011

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“…An extension of the present approach to defect recognition from radiographic or ultrasonic images can be achieved based on generalizations of the fluctuation analyses to measure surface roughness [18,19]. Given any two-dimensional image, a corresponding surface can be built by a color-toheight conversion procedure, and mathematical analyses can then be performed.…”

Section: Discussionmentioning

confidence: 99%

Fluctuation Analyses for Pattern Classification in Nondestructive Materials Inspection

Vieira

Moura

Gonçalves

2010

EURASIP J. Adv. Signal Process.

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We review recent work on the application of fluctuation analyses of time series for pattern classification in nondestructive materials inspection. These analyses are based on the evaluation of time-series fluctuations across time intervals of increasing size, and were originally introduced in the study of fractals. A number of examples indicate that this approach yields relevant features allowing the successful classification of patterns such as (i) microstructure signatures in cast irons, as probed by backscattered ultrasonic signals; (ii) welding defects in metals, as probed by TOFD ultrasonic signals; (iii) gear faults, based on vibration signals; (iv) weld-transfer modes, as probed by voltage and current time series; (v) microstructural composition in stainless steel, as probed by magnetic Barkhausen noise and magnetic flux signals.

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“…The KL transformation [9] consists of initially projecting the training vectors along the eigenvectors of the within-class covariance matrix W S , defined by (18) where C N is the number of different classes, k N is the number of vectors in class k, k m is the average vector of class k, and T denotes the transpose of a matrix (in this case, of a column vector). The KL transformation [9] consists of initially projecting the training vectors along the eigenvectors of the within-class covariance matrix W S , defined by (18) where C N is the number of different classes, k N is the number of vectors in class k, k m is the average vector of class k, and T denotes the transpose of a matrix (in this case, of a column vector).…”

Section: Karhunen-loève (Kl) Transformationmentioning

confidence: 99%

Characterization of Cast Iron Microstructure Through the Statistical Fluctuation and Fractal Analyses of Ultrasonic Backscattered Signals

Moura¹,

Normando²,

Gonçalves³

et al. 2011

AIP Conference Proceedings

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Correlation of ultrasonic scatterer size estimates for the statistical analysis and optimization of angular compounding ABSTRACT: This work aims to identify the different microstructures presented by cast iron, namely, lamellar, vermicular and spheroidal microstructures, through the statistical fluctuation and fractal analyses of backscattered ultrasonic signals. The signals were obtained with a broadband direct contact ultrasonic probe with a central frequency of 5 MHz. The statistical fluctuations of the ultrasonic signals were analyzed using Hurst and detrended-fluctuation analyses (DFA), and the fractal analyses were carried out by applying the minimal cover and box-counting techniques to the signals. The curves obtained for the statistical fluctuations and fractal analyses, as function of time window, were processed by using two pattern classification techniques, namely, principal-component analysis (PCA) and Karhunen-Loève expansion. For the Karhunen-Loève expansion, an approximately 100% success rate has been reached for the classification of the different microstructures, for the training and the testing sets of events. The results presented correspond to an average taken over a 100 randomly chosen sets of events. It is concluded that the statistical fluctuation and fractal analyses are effective additional tools for recognition of the different cast iron microstructures.

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Fractal Analysis of Weld Defect Patterns Obtained by Radiographic Tests

Cited by 4 publications

References 9 publications

Characterization of Cast Iron Microstructure Through Fluctuation and Fractal Analyses of Ultrasonic Backscattered Signals Combined with Classification Techniques

Characterization of Cast Iron Microstructure Through Fluctuation and Fractal Analyses of Ultrasonic Backscattered Signals Combined with Classification Techniques

Fluctuation Analyses for Pattern Classification in Nondestructive Materials Inspection

Characterization of Cast Iron Microstructure Through the Statistical Fluctuation and Fractal Analyses of Ultrasonic Backscattered Signals

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