| Title |
Detection and classification of uniform and concentrated wall-thinning defects using high-order circumferential guided waves and artificial neural networks / |
| Authors |
Cirtautas, Donatas ; Samaitis, Vykintas ; Mažeika, Liudas ; Raišutis, Renaldas |
| DOI |
10.3390/s23146505 |
| Full Text |
|
| Is Part of |
Sensors.. Basel : MDPI. 2023, vol. 23, iss. 14, art. no. 6505, p. 1-22.. ISSN 1424-8220. eISSN 1424-8220 |
| Keywords [eng] |
artificial neural networks ; corrosion ; defect classification ; high-order modes ; pipeline structures ; ultrasonic guided waves ; wall thinning |
| Abstract [eng] |
Pipeline structures are susceptible to corrosion, leading to significant safety, environmental, and economic implications. Existing long range guided wave inspection systems often fail to detect footprints of the concentrated defects, which can lead to leakage. One way to tackle this issue is the utilization of circumferential guided waves that inspect the pipe’s cross section. However, achieving the necessary detection resolution typically necessitates the use of high-order modes hindering the inspection data interpretation. This study presents the implementation of an ultrasonic technique capable of detecting and classifying wall thinning and concentrated defects using high-order guided wave modes. The technique is based on a proposed phase velocity mapping approach, which generates a set of isolated wave modes within a specified phase velocity range. By referencing phase velocity maps obtained from defect-free stages of the pipe, it becomes possible to observe changes resulting from the presence of defects and assign those changes to the specific type of damage using artificial neural networks (ANN). The paper outlines the fundamental principles of the proposed phase velocity mapping technique and the ANN models employed for classification tasks that use synthetic data as an input. The presented results are meticulously verified using samples with artificial defects and appropriate numerical models. Through numerical modeling, experimental verification, and analysis using ANN, the proposed method demonstrates promising outcomes in defect detection and classification, providing a more comprehensive assessment of wall thinning and concentrated defects. The model achieved an average prediction accuracy of 92% for localized defects, 99% for defect-free cases, and 98% for uniform defects. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2023 |
| CC license |
|
