_applied_  
_sciences_  
_Article_  
**Automated Classification of Ultrasonic Signal via a Convolutional Neural Network**  
**[PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 2,*  
1 School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China; [EMAIL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (W.X. ); [EMAIL_ADDRESS] (J.Z.) 2 School of Materials and Engineering, Southeast University, Nanjing 211189, China
* Correspondence: [EMAIL_ADDRESS]  
**Abstract: Ultrasonic signal classification in nondestructive testing is of great significance for the detection of defects. The current methods have mainly utilized low-level handcrafted features based on traditional signal processing approaches, such as the Fourier transform, wavelet transform and the like, to interpret the information carried by signals for classification. This paper proposes an automatic classification method via a convolutional neural network (CNN) which can automatically extract features from raw data to classify ultrasonic signals collected of a circumferential weld composed of austenitic and martensitic stainless steel with internal slots. Experiments demonstrate that our method outperforms the traditional classifier with manually extracted features, achieving an accuracy rate of classification up to 0.982. Furthermore, we visualize the shape, location and orientation of defects with a C-scan imaging process based on classification results, validating the effectiveness of the results. **  
**Keywords: ultrasonic signal; automated classification; features; signal processing; convolutional neural network**  
**List of Publications: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,  
are a selection of the shape parameters obtained from the pulse-echo by [PERSON] analysis. [PERSON] et al. [6] extracted features for discrimination of detected echoes from the perspective of time domain, spectral domain and discrete wavelet representation. The compact feature vector obtained is then classified by different methods: the K nearest neighbor algorithm, the statistical Bayesian algorithm and artificial neural networks. Back-propagation neural networks are trained by the characteristic parameters extracted from ultrasonic signals to determine the type, location and length of cracks in the medium [7]. [PERSON] et al. [8] developed a feed forward neural network along with wavelet blind separation to classify positions, width and depth of defects in not accessible pipes. [PERSON] et al. [9] chose the peak amplitude, the energy of the signal and the time of the flight of ultrasonic echo signals as the feature indicators and developed a cascade feed forward back-propagation neural network model to estimate both crack size and crack location simultaneously. Two-dimensional information about different types of defects was collected via the wavelet transform and then used to improve automatic ultrasonic flaw detection and classification accuracies using the neural network [3]. [PERSON] et al. [10] presented automatic ultrasonic flaw signal classification models based on ANNs and support vector machines (SVMs) by using wavelet-transform-based strategies for feature extraction. [PERSON] et al. [11] applied the wavelet packet transform to extract feature vectors representing defect qualities and then utilized that data to achieve the accurate classification of the welding defects an SVM-based radial basis function neural network. Although the above-mentioned approaches have achieved good performance for automatic signal classification, it is necessary to choose and extract either time- or frequency-domain features from ultrasonic signals for training the neural network algorithm. However, this feature extraction is highly subjective and empirical, and in most cases, it is difficult to extract the reliable features that are most effective or relevant with the defects to be detected. In addition, it is not practical for industrial application since the workload of feature extraction for training neural networks increases dramatically under large amounts of original data.