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Acoustic emission entropy: An innovative approach for structural health monitoring of fracture‐critical metallic components subjected to fatigue loading

Acoustic emission entropy: An innovative approach for structural health monitoring of fracture‐critical metallic components subjected to fatigue loading

D'Angela, Marianna and Ercolino, Marianna ORCID: 0000-0001-8678-0631 (2021) Acoustic emission entropy: An innovative approach for structural health monitoring of fracture‐critical metallic components subjected to fatigue loading. Fatigue & Fracture of Engineering Materials & Structures, 44 (4). pp. 1041-1058. ISSN 8756-758X (Print), 1460-2695 (Online) (doi:https://doi.org/10.1111/ffe.13412)

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Abstract

The paper presents an innovative approach for structural health monitoring of metallic components under fatigue crack phenomena. The methodology is based on the evaluation of the information entropy of the acoustic emission (AE) data. AE testing of fatigue crack growth (FCG) is performed on metallic components is performed within an extremely noisy testing environment. Basic AE data analysis is demonstrated to be inefficient with regard to the specific testing conditions. AE entropy is proven to be a reliable damage‐sensitive feature for real‐time assessment despite both significant noise disturbance and complexity/randomness of the acoustic phenomena. This was also confirmed for (time‐)discontinuous monitoring processes over random‐based data detections. An innovative monitoring protocol is finally developed according to the experimental evidence also considering the recommendations of the current monitoring. The protocol is found to be promising for structural health monitoring of metallic fracture‐critical components of structures under fatigue.

Item Type: Article
Uncontrolled Keywords: acoustic emission, information entropy, fatigue crack, metallic components, structural health monitoring
Subjects: T Technology > TH Building construction
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 10 May 2021 18:22
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/30900

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