Classification of ultrasonic signals
In ultrasonic non-destructive testing it is very difficult to detect flaws in materials with coarse-grain structure. The ultrasonic signals measured on these materials contain echoes which are very similar to fault echoes. These echoes arise from grains which are contained in the material. For the detection of flaws various methods for suppressing echoes from grains have to be used. In this work we used the method for filtering ultrasonic signals based on discrete wavelet transform. For the classification of ultrasonic signals in A-scan we used a pattern recognition method called support vector machines. In this study we classify signals with fault echoes, echo from weld and back-wall echo. Ultrasonic signals were measured on materials used for constructing aeroplane engines. The experimental results indicate the performance of the proposed approach.
Wavelet Transform Based Feature Extraction for Ultrasonic Flaw Signal Classification
Abstract—In this paper, we present a automatic system to classify the ultrasonic flaw signals acquired from carbon fiber reinforced polymer (CFRP) specimens with void, delamination and debonding. Different strategies based on discrete wavelet transform (DWT) and wavelet packet transform (WPT) are utilized for feature extraction. After that, the linear mapping has been applied from wavelet domain to principal components analysis (PCA) domain for dimension reduction. Artificial neural networks (ANNs) and support vector machines (SVMs) are trained to validate the effectiveness of different wavelet transform based features for flaw signal classification. Experimental results show that the normalized energy of WPT coefficients and statistical parameters of WPT representation of original signal can construct the reliable features to effectively classify the different ultrasonic flaw signals with high accuracy and low training elapsed time.
Analysis of a non-linear frequency diverse clutter suppression algorithm
Abstract The target signal is often received in the presence of coherent noise resulting from a large number of complex and randomly distributed scatterers known as clutter, whose echoes can suppress or mask the actual target echo. This is a fundamental limitation which exists in all imaging and detection applications and cannot be removed by conventional techniques, such as time averaging. This paper examines a non-linear signal processing algorithm (polarity thresholding) used in conjunction with a frequency diversity technique (split-spectrum processing), which decorrelates clutter. Since the target echo exhibits significantly smaller amplitude variations with frequency compared to clutter, the polarity thresholding algorithm can achieve flaw enhancement by setting the output to zero at time instants where phase reversal occurs. Theoretical derivations are provided to determine the signal to noise ratio enhancement capabilities of the algorithm. Ultrasonic data obtained from large grained stainless-steel samples with flat-bottom holes are used to verify the theoretical results and demonstrate the grain echo suppression capability of the algorithm in imaging applications.