Search

Computer aided Interpretation of Ultrasonic signals depicting flaws in weld pieces is depicted in this work. In this work, feasibility of Long Short Term Memory (LSTM) for flaw characterization is studied. Owing to the advantage of LSTM, the first technique involves training LSTM directly with the signals as inputs and testing its ability to characterize the flaws from the input signals. Due to wide variation in the length of input sequences, which introduced sparseness in other sequences, overall accuracy is affected. Hence in the second technique, LSTM are trained with features of the signals and it is found that the overall accuracy for test data is 67.64%. These features are statistical parameters obtained from the approximation co-efficient of the input signals. The input signals are decomposed with a novel wavelet template.

Industrial Radiography is the most widely used NDT technique for weld defect characterization. As it is an indirect technique, an expert radiologist is required at the interpretation end to identify and characterize the defect in radiographs. However radiographs suffer from poor contrast thereby making it difficult to isolate the defect region from the background. Hence it is necessary to develop computer aided analysis tool for automated interpretation of weld radiographs. It involves an efficient contrast enhancement and segmentation technique for isolating the weld defect to its true size. In this work, wavelet based contrast enhancement technique has been developed to highlight the defect region from background. Biorthogonal 6.8 wavelet and Daubechies wavelet of order 4 are used for enhancing root/slag and porosity respectively. Thresholding and region growing are used for segmenting the region of interest.

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.

Epileptic seizure can be detected by many ways but EEG signal prove to be the most important marker. Since EEG signal requires a strenuous effort to go through pages of recorded signal. Automatic seizure detection can be done by extracting features from the EEG signals and then feeding them to the supervised learning algorithms for classification and prediction. In this paper the features that are chosen are mean, standard deviation, skewness, kurtosis, interquartile range and mean absolute deviation. A comparative study of SVM and GRNN are done in this work and GRNN proves to be accurate for seizure detection applications.