Diagnosis of multiple faults in rotating machinery using ensemble learning

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dc.contributor.authorInyang, Udeme Ibanga
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorJennions, Ian
dc.date.accessioned2023-02-01T12:56:20Z
dc.date.available2023-02-01T12:56:20Z
dc.date.issued2023-01-15
dc.description.abstractFault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.en_UK
dc.identifier.citationInyang UI, Petrunin I, Jennions I. (2023) Diagnosis of multiple faults in rotating machinery using ensemble learning. Sensors, Volume 23, Issue 2, January 2023, Article number 1005en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s23021005
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19081
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectcomprehensiveen_UK
dc.subjectmultiple faultsen_UK
dc.subjectgearen_UK
dc.subjectbearingen_UK
dc.subjectshaften_UK
dc.subjectoptimizationen_UK
dc.subjectscalesen_UK
dc.titleDiagnosis of multiple faults in rotating machinery using ensemble learningen_UK
dc.typeArticleen_UK

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