Application of classification neural networks for identifcation of damage stages of degraded low alloy steel based on acoustic emission data analysis / Joanna Krajewska‑Śpiewak, Igor Lasota, Barbara ... więcej

Application of classification neural networks for identifcation of damage stages of degraded low alloy steel based on acoustic emission data analysis / Joanna Krajewska‑Śpiewak, Igor Lasota, Barbara Kozub // Archives of Civil and Mechanical Engineering [Dokument elektroniczny]. – 2020, Vol. 20, Iss. 4, 10 s. : il. (w tym kolor.). – Tryb dostępu: https://link.springer.com/article/10.1007/s43452-020-00112-3. – Oznaczenie ref./art.: 109. – Bibliogr. 23 poz., Abstr. – doi: 10.1007/s43452-020-00112-3. – e-ISSN 2083-3318

The paper presents the infuence of low alloy steel degradation on the acoustic emission (AE) generated during static tension of notched specimen. The material was cut from a technological pipeline long-term ... więcej

The paper presents the infuence of low alloy steel degradation on the acoustic emission (AE) generated during static tension of notched specimen. The material was cut from a technological pipeline long-term operated in the oil refnery industry. Comparative analysis of AE activity generated by damage process of degraded and new material has been carried out. The diferent AE parameters were used to detect diferent stages of fracture process of low alloy steel under quasi-static tensile test. Neural networks with three layers were created with Broyden–Fletcher–Goldfarb–Shanno learning algorithm for a database analysis. The diferent AE parameters were included in the input layer. Classifcation neural networks were created in order to determine the stages of material degradation. The results obtained from the carried out studies will be used as the basis for new methodology development of the assessment of the structural condition of in-service equipment.