• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.1
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• pp.8-14
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• 2004

Model-based inversion tools for eddy current signals have been developed by combining neural networks and finite element modeling, for quantitative flaw characterization in steam generator tubes. In the present work, interpretation of experimental eddy current signals was carried out in order to validate the developed inversion tools. A database was constructed using the synthetic flaw signals generated by the finite element model. The hybrid neural networks composed of a PNN classifier and BPNN size estimators were trained using the synthetic signals. Experimental eddy current signals were obtained from axisymmetric artificial flaws. Interpretation of flaw signals was conducted by feeding the experimental signals into the neural networks. The interpretation was excellent, which shows that the developed inversion tools would be applicable to the Interpretation of real eddy current signals.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.5
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• pp.479-483
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• 2010

9% nickel alloy steels used for LNG, cryogenic liquid, storage tank are welded with dissimilar Inconel or Hastelloy welding rod and the weldment shows similar characteristic with the dissimilar metal weld of low carbon steel and austenitic stainless steel. Band type indications are sometimes shown on the film during radiography test of the weldments. Thus this study identified whether the indications are non-relevant indications through material, radiographic test, ultrasonic test, liquid penetrant test and microstructure analysis and also proposed radiography film interpretation and cause of band type indications.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.386-391
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• 2006

This study presents experimental results of sloshing phenomenon done on rectangular box. A simple harmonic excitation was done on the box. Two kinds of filling ratio, 20% and 30% of height, were tested. A total of 15 pressure sensors were installed to monitor the impact pressure. Each test was repeated for 20 times to ensure the repeatability. The high speed camera captured the flaw filed and the corresponding pressure were synchronize with video signal so that the video image can help the interpretation of the impact pressure. The two filling ratio made difference in the flaw characteristic and impact pressure. The use of high speed camera made it possible to understand the bubble generation mechanism. The pressure time histories were presented.

• Journal of the Korean Society for Nondestructive Testing
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• v.13 no.3
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• pp.24-30
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• 1993

The necessity of ultrasonic inspection to detect the cracks in turbine blade is being increased as the forced outage of nuclear power plants have been occurred due to blade failure in turbine components. However, the complex blade root geometry causes the ultrasonic inspection technique not to be established yet and much effort is required to set up a more reliable inspection. In this paper, the ultrasonic inspection technique for flaw detectability, skew angle effect, identification of flaw and geometric signal have been investigated with a test block and discussed the interpretation of ultrasonic signal through the acquisition and analysis of RF waveform. The experimental results show that the proper examination procedure can be established. It is required that the skew angle is essential to decrease the effect of signals from the complex blade geometry. The present results of this study can be applied to the site inspection without blade disassembly.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.236-241
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• 1996

This paper presents a new eddy current testing system for inspecting tubes of steam generator in nuclear power plant. The proposed system adopted embedded expert system concept to automate tasks of the inspection such as inspection planning and flaw signal interpretation, and integrated all the tasks into a client/server type computing architecture using database management system. Therefore, human factor errors occurred during inspection could be minimized and the inspection data could be transferred in real-time. As a result, we can increase the level of inspection confidence and the productivity of a personal inspector. A prototype of the proposed system has been developed for 5 years and the test operation has been performed in domestic nuclear power plants.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.15-26
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• 2003

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an alternative approach which uses the least mean square (LMS) method and expectation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximization (SAGE) algorithm In conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.1
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• pp.27-35
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• 2005

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature spare. This paper describes an alternative approach which uses the least mean square (LMS) method and exportation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximiBation (SAGE) algorithm ill conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor. Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.