Application of classification neural networks for identifcation of damage stages of degraded low alloy steel based on acoustic emission data analysis
Authors:
- Joanna Krajewska-Śpiewak,
- Igor Lasota,
- Barbara Kozub
Abstract
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.
- Record ID
- CUTcdf4738b4e374156bd09cdf241b9fada
- Publication categories
- ;
- Author
- Journal series
- Archives of Civil and Mechanical Engineering, ISSN 1644-9665, e-ISSN 2083-3318
- Issue year
- 2020
- Vol
- 20
- No
- 4
- Pages
- [1-10]
- Article number
- 109
- Other elements of collation
- il. (w tym kolor.); Bibliografia (na s.) - 9-10; Bibliografia (liczba pozycji) - 23; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 20, Iss. 4
- Keywords in English
- acoustic emission, classification neural networks, refinery industry, ferritic alloy steel, plastic deformation
- DOI
- DOI:10.1007/s43452-020-00112-3 Opening in a new tab
- URL
- https://link.springer.com/article/10.1007/s43452-020-00112-3 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTcdf4738b4e374156bd09cdf241b9fada/
- URN
urn:pkr-prod:CUTcdf4738b4e374156bd09cdf241b9fada
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.