Automatic Quantification of Subsurface Defects by Analyzing Laser Ultrasonic Signals Using Convolutional Neural Networks and Wavelet Transform
The conventional machine learning algorithm for analyzing ultrasonic signals to detect structural defects necessarily identifies and extracts either time- or frequency-domain features manually, which has problems in reliability and effectiveness. This work proposes a novel approach by combining conv...
| Publié dans: | IEEE transactions on ultrasonics, ferroelectrics, and frequency control. - 1986. - 68(2021), 10 vom: 09. Okt., Seite 3216-3225 |
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| Auteur principal: | |
| Autres auteurs: | , , , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2021
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| Accès à la collection: | IEEE transactions on ultrasonics, ferroelectrics, and frequency control |
| Sujets: | Journal Article Research Support, Non-U.S. Gov't |
| Résumé: | The conventional machine learning algorithm for analyzing ultrasonic signals to detect structural defects necessarily identifies and extracts either time- or frequency-domain features manually, which has problems in reliability and effectiveness. This work proposes a novel approach by combining convolutional neural networks (CNNs) and wavelet transform to analyze the laser-generated ultrasonic signals for detecting the width of subsurface defects accurately. The novelty of this work is to convert the laser ultrasonic signals into the scalograms (images) via wavelet transform, which are subsequently utilized as the image input for the pretrained CNN to extract the defect features automatically to quantify the width of defects, avoiding the necessity and inaccuracy induced by artificial feature selection. The experimentally validated numerical model that simulates the interaction of laser-generated ultrasonic waves with subsurface defects is first established, which is further utilized to generate adequate laser ultrasonic signals for training the CNN model. A total number of 3104 data are obtained from simulation and experiments, with 2480 simulated signals for training the CNN model and the remaining 620 simulated data together with 4 experimental signals for verifying the performance of the proposed algorithm. This approach achieves the prediction accuracy of 98.5% on validation set, particularly with the prediction accuracy of 100% for the four experimental data. This work proves the feasibility and reliability of the proposed method for quantifying the width of subsurface defects and can be further expanded as a universal approach to various other defects detection, such as defect locations and shapes |
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| Description: | Date Completed 01.11.2021 Date Revised 01.11.2021 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1525-8955 |
| DOI: | 10.1109/TUFFC.2021.3087949 |