engleski

Detection of Defective Bolts from Rotational Ultrasonic Scans Using Convolutional Neural Networks

Bolts are one of the primary components used when constructing complex systems such as power plants, factories, railways, and similar. Due to constant stress over the years, various types of defects can appear inside bolts, making the overall structure unsafe. Detection of defective bolts can be done by employing a non-destructive material evaluation technique, such as ultrasonic testing (UT). However, the amount of data acquired during the inspection is often large, so the analysis, nowadays performed manually, lasts a long time. In this work, we propose a method based on a convolutional neural network (CNN) to classify ultrasonic scans and detect defective bolts. We propose a novel representation of the ultrasonic B-scans that we call rotational B-scans. By transforming the original database of B-scans into this novel representation, the number of images displaying a defect increases. This balances the dataset, decreases the dataset variance, and makes the training of a deep convolutional neural network significantly easier. We tested many existing architectures and based on our findings we designed a custom encoder-decoder-based classifier. Our model outperformed all the other tested models and reached an area under the receiver operating characteristic curve (AUC-ROC) of 97.4%.

non-destructive testing, ultrasonic scans, deep learning

nije evidentirano