A composite learning approach for multiple fault diagnosis in gears

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dc.contributor.authorInyang, Udeme Ibanga
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorJennions, Ian
dc.date.accessioned2023-01-06T10:59:15Z
dc.date.available2023-01-06T10:59:15Z
dc.date.issued2022-11-05
dc.description.abstractA major part of Prognostic and Health Management of rotating machines is dedicated to diagnosis operations. This makes early and accurate diagnosis of single and multiple faults an economically important requirement of many industries. With the well-known challenges of multiple faults, this paper proposes a new Blended Ensemble Convolutional Neural Network – Support Vector Machine (BECNN-SVM) model for multiple and single faults diagnosis of gears. The proposed approach is obtained by preprocessing the acquired signals using complementary signal processing techniques. This form inputs to 2D Convolutional Neural Network base learners which are fused through a blended ensemble model for fault detection in gears. Discriminative properties of the complementary features ensure the high capabilities of the approach to give good results under different load, speed, and fault conditions of the gear system. The experimental results show that the proposed method can accurately detect rotating machine faults. The proposed approach compared with other state-of-the-art methods indicates improved overall effectiveness for gear faults diagnosis.en_UK
dc.identifier.citationInyang UI, Petrunin I, Jennions I. (2024) A composite learning approach for multiple fault diagnosis in gears. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, Volume 238, Issue 1, February 2024, pp. 158-171en_UK
dc.identifier.issn1748-006X
dc.identifier.urihttps://doi.org/10.1177/1748006X221129954
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18890
dc.language.isoenen_UK
dc.publisherSAGEen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectGearsen_UK
dc.subjectcomplementaryen_UK
dc.subjectdiagnosisen_UK
dc.subjectblending ensembleen_UK
dc.subjectmultiple faultsen_UK
dc.titleA composite learning approach for multiple fault diagnosis in gearsen_UK
dc.typeArticleen_UK

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