<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>Pulse-echo ultrasonic signal is used to detect weld defects with high probability. However, utilizing echo signal for defects classification is another issue that has attracted attention of many researchers who have devised algorithms and tested them against their own databases. In this paper, a study is conducted to score the performance of various algorithms against a single echo signal database. Algorithms tested the use of Wavelet Transform (WT), Fast Fourier Transform (FFT) and time domain echo signal features and employed several NN’s architectures such as Multi-Layer Perceptron Neural Network (MLP), Self Organizing Map (SOM) and others known to be good classifiers. The average performance of all can be viewed fair (90%) while some algorithms render success rate of about 94%. It seems that acquiring higher success rates out of a single fixed angle probe pulseecho set up needs new arrangements of data collection, which is under investigation.</p></abstract>

Pulse-echo ultrasonic signal is used to detect weld defects with high probability. However, utilizing echo signal for defects classification is another issue that has attracted attention of many researchers who have devised algorithms and tested them against their own databases. In this paper, a study is conducted to score the performance of various algorithms against a single echo signal database. Algorithms tested the use of Wavelet Transform (WT), Fast Fourier Transform (FFT) and time domain echo signal features and employed several NN’s architectures such as Multi-Layer Perceptron Neural Network (MLP), Self Organizing Map (SOM) and others known to be good classifiers. The average performance of all can be viewed fair (90%) while some algorithms render success rate of about 94%. It seems that acquiring higher success rates out of a single fixed angle probe pulseecho set up needs new arrangements of data collection, which is under investigation.

Performance Evaluation of Neural Network Based Pulse-Echo Weld Defect Classifiers

Engineering Faculty, Shahed University, Tehran, P.O.Box 18155/159, Iran

Measurement Science Review

Pulse-echo ultrasonic signal is used to detect weld defects with high probability. However, utilizing echo signal for defects classification is another issue...

Performance Evaluation of Neural Network Based Pulse-Echo Weld Defect Classifiers

Data publikacji: 21 paź 2012

Zakres stron: 168 - 174

DOI: https://doi.org/10.2478/v10048-012-0026-5

Słowa kluczowe
Nondestructive testing, Pulse-Echo ultrasonic, neural network, radial basis function, self organizing map, Wavelet Transform

This content is open access.

Performance Evaluation of Neural Network Based Pulse-Echo Weld Defect Classifiers

Data publikacji: 21 paź 2012

Zakres stron: 168 - 174

DOI: https://doi.org/10.2478/v10048-012-0026-5

Słowa kluczoweNondestructive testing, Pulse-Echo ultrasonic, neural network, radial basis function, self organizing map, Wavelet Transform

This content is open access.

Słowa kluczowe
Nondestructive testing, Pulse-Echo ultrasonic, neural network, radial basis function, self organizing map, Wavelet Transform