Phased Array Ultrasonic Test Signal Enhancement and Classification Using Empirical Wavelet Transform And Deep Convolution Neural Network

In the recent past, Non-Destructive Testing (NDT) has become a most popular technology due to its efficiency and accuracy without destroying the object and maintains its original structure and gathering while examining external and internal welding defects. Generally, the NDT environment is harmful which is distinguished by huge volatile fields of electromagnetic, elevated radiation emission instability and elevated heat. Therefore, a suitable NDT approach could be recognized and practiced. In this paper, a novel algorithm is proposed based on Phased array ultrasonic test (PAUT) for NDT in order to attain the proper test attributes. In the proposed methodology, carbon steel welding section is synthetically produced with various defects and tested using PAUT method. The signals acquired from PAUT device is found with noise interference. The Adaptive Least Mean Square (ALMS) filter is proposed to filter PAUT signal in order to eliminate random noise and Gaussian noise. The ALMS filter is the combination of low pass filter (LPF), high pass filter (HPF) and band pass filter (BPF). The time domain PAUT signal is converted into frequency domain signal in order to extract more number of features by applying Empirical Wavelet Transform (EWT) algorithm. In the frequency domain signal, 1st order and 2nd order features extraction techniques are applied to extract various features for further classification. The Deep Learning methodology is proposed for classification PAUT signals. Based on the PAUT signal features, the Deep Convolution Neural Network (DCNN) is applied for further classification. The DCNN will classify the welding signal is whether it is defective or non-defective. The Confusion Matrix (CM) is used for estimation of measurement of performance of classification as calculating accuracy, sensitivity and specificity. The experiments prove that out proposed methodology for PAUT testing for welding defect classification is obtained more accurately and efficiently across existing methodologies by providing numerical and graphical results.