Identification of the Fracture Process in Gas Pipeline Steel Based on the Analysis of AE Signals

• Published in: Materials Vol. 15; no. 7; p. 2659
• Main Authors: Świt, Grzegorz, Dzioba, Ihor, Adamczak-Bugno, Anna, Krampikowska, Aleksandra
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04.04.2022; MDPI
• Subjects: Acoustic emission; Acoustics; Cluster analysis; Clustering; Damage detection; Elongation; Frequency spectrum; Gas pipelines; Laboratories; Natural gas; Sensors; Signal processing; Structural steels; Tensile strength; Time-frequency analysis; Vector quantization; gas pipeline steel; acoustic emission; fracture process; time-frequency analysis; wavelet analysis; k-means algorithm
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma15072659

The paper presents the results of tests conducted to identify the damage process in specimens collected from the steel of a gas pipeline. The tests concerned specimens made of S235 steel subject to quasi-static loading—uniaxial tension until failure. Acoustic emission (AE) signals were recorded during the loading process along with force and elongation signals. Sections were collected from previously loaded specimens and subjected to microstructural examinations to determine the nature of material damage at different strain stages. The recorded AE signals were analyzed using the k-means clustering method, as well as time-frequency analysis. The results of metallographic tests and analysis of AE signals identified frequency spectra characteristic of different stages of the process of material damage.