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dc.contributor.authorSepulveda, Natalia Espinoza; email: natalia.espinozasepulveda@manchester.ac.uk
dc.contributor.authorSinha, Jyoti; orcid: 0000-0001-9202-1789; email: jyoti.sinha@manchester.ac.uk
dc.date.accessioned2021-08-08T23:27:18Z
dc.date.available2021-08-08T23:27:18Z
dc.date.issued2021-08-07
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/625522/machines-09-00155.pdf?sequence=2
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/625522/machines-09-00155.xml?sequence=3
dc.identifier.citationMachines, volume 9, issue 8, page e155
dc.identifier.urihttp://hdl.handle.net/10034/625522
dc.descriptionFrom MDPI via Jisc Publications Router
dc.descriptionHistory: accepted 2021-08-03, pub-electronic 2021-08-07
dc.descriptionPublication status: Published
dc.description.abstractMathematical models have been widely used in the study of rotating machines. Their application in dynamics has eased further research since they can avoid time-consuming and exorbitant experimental processes to simulate different faults. The earlier vibration-based machine-learning (VML) model for fault diagnosis in rotating machines was developed by optimising the vibration-based parameters from experimental data on a rig. Therefore, a mathematical model based on the finite-element (FE) method is created for the experimental rig, to simulate several rotor-related faults. The generated vibration responses in the FE model are then used to validate the earlier developed fault diagnosis model and the optimised parameters. The obtained results suggest the correctness of the selected parameters to characterise the dynamics of the machine to identify faults. These promising results provide the possibility of implementing the VML model in real industrial systems.
dc.languageen
dc.publisherMDPI
dc.rightsLicence for this article: https://creativecommons.org/licenses/by/4.0/
dc.sourceeissn: 2075-1702
dc.subjectrotating machine
dc.subjectrotor faults
dc.subjectfault diagnosis
dc.subjectfinite-element model
dc.subjectmathematical simulation
dc.subjectmachine learning
dc.titleMathematical Validation of Experimentally Optimised Parameters Used in a Vibration-Based Machine-Learning Model for Fault Diagnosis in Rotating Machines
dc.typearticle
dc.date.updated2021-08-08T23:27:18Z
dc.date.accepted2021-08-03


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