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Ultrasonic Testing is the highly reliable Non Destructive Testing (NDT) technique for flaw detection in steel weldments. Ultrasonic test signals are analyzes to identify the nature of the defect. In this paper, Hilbert transform is used for decomposing the ultrasonic test signals into high and low frequency components (namely Intrinsic Mode Function). These components are characterised in terms of Power Spectral Density (PSD). An attempt has been successfully made to classify the flaws based on PSD. It is found that power spectral density of planar defects is higher than volumetric defects for the fourth Intrinsic Mode Function.

Non-destructive testing (NDT) techniques are most commonly employed for detection and characterization of flaws in the component. Apart from flaw characteristics, another parameter which is equally important to assess the structural integrity of engineering components is the material property. Nondestructive testing techniques offer several advantages over the conventional coupon-based techniques. Ultrasonic Testing is one of the widely used NDT techniques for material characterization. In this paper flaw detection such as lack of fusion and slag of welding carbonized steel and stainless steel material is characterized using discrete wavelet transform (DWT).In order to categorize the type of flaws, the given ultrasonic signal is subjected into DWT decomposition. Then, energy is calculated from DWT approximation coefficient. Finally corresponding welding flaw is determined using computed energy.

Ultrasonic Testing is the widely used NDT technique for flaw detection in thick walled weldments. It is an indirect technique and the signals are to be analyzed in order to characterize the flaw. Manual interpretation of these signals is subjective in nature and is dependent on the expertise of the individual. Hence the paradigm has shifted to automated signal analysis. In this paper a successful attempt has been made to develop a pattern among the flaws of same type without using Artificial Neural Networks. Here, the signals are analyzed with Stockwell transform and the pattern is determined. Also quantitative characterization is done with mean, standard deviation, root mean square value, peak to rms ratio.

Ultrasonic Testing is a highly reliable Non-Destructive Testing Technique for weld defect characterization. Defects occur either high frequency components (Porosity, Sidewall crack) or as low frequency components (Root, Lack of Fusion, Lack of penetration, slag) in the UT signal. Manual interpretation of these signals is subjective in nature and is dependent on the expertise of the individual. Hence it is necessary to develop automated signal analysis system that classifies the defect. As defect classification is non-linear in nature, neural network based classification techniques are cited in literature. However neural network based techniques are computationally complex and has prediction error. Hence in this paper, an effective range based classification system using statistical moments is proposed. Performance of the proposed technique is measured in terms sensitivity and specificity.