A machine learning approach to model interdependencies between dynamic response and crack propagation

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dc.contributor.authorFleet, Thomas
dc.contributor.authorKamei, Khangamlung
dc.contributor.authorHe, Feiyang
dc.contributor.authorKhan, Muhammad A.
dc.contributor.authorKhan, Kamran Ahmed
dc.contributor.authorStarr, Andrew
dc.date.accessioned2021-01-19T15:15:16Z
dc.date.available2021-01-19T15:15:16Z
dc.date.issued2020-11-30
dc.description.abstractAccurate damage detection in engineering structures is a critical part of structural health monitoring. A variety of non-destructive inspection methods has been employed to detect the presence and severity of the damage. In this research, machine learning (ML) algorithms are used to assess the dynamic response of the system. It can predict the damage severity, damage location, and fundamental behaviour of the system. Fatigue damage data of aluminium and ABS under coupled mechanical loads at different temperatures are used to train the model. The model shows that natural frequency and temperature appear to be the most important predictive features for aluminium. It appears to be dominated by natural frequency and tip amplitude for ABS. The results also show that the position of the crack along the specimen appears to be of little importance for either material, allowing simultaneous prediction of location and damage severityen_UK
dc.identifier.citationFleet T, Kamei K, He F, et al., (2020) A machine learning approach to model interdependencies between dynamic response and crack propagation, Sensors, Volume 20, Issue 23, 2020, Article number 6847en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s20236847
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16202
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectdamage detectionen_UK
dc.subjectfatigue crack growthen_UK
dc.subjectthermomechanical fatigueen_UK
dc.subjectmachine learningen_UK
dc.titleA machine learning approach to model interdependencies between dynamic response and crack propagationen_UK
dc.typeArticleen_UK

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