Wear particle image analysis: feature extraction, selection and classification by deep and machine learning

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dc.catalogadoraba
dc.contributor.authorVivek J.
dc.contributor.authorVenkatesh S N.
dc.contributor.authorMahanta T.K.
dc.contributor.authorV S.
dc.contributor.authorAmarnath M.
dc.contributor.authorRamteke R., Sangharatna Munneshwar
dc.contributor.authorMarian, Max
dc.date.accessioned2025-04-01T12:48:21Z
dc.date.available2025-04-01T12:48:21Z
dc.date.issued2024
dc.description.abstractPurpose: This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational efficiency through wear image analysis. Design/methodology/approach: Using a data set of scanning electron microscopy images from an internal combustion engine, the authors used AlexNet as the feature extraction algorithm and the J48 decision tree algorithm for feature selection and compared 15 ML classifiers from the lazy-, Bayes and tree-based families. Findings: From the analyzed ML classifiers, instance-based k-nearest neighbor emerged as the optimal algorithm with a 95% classification accuracy against testing data. This surpassed individually trained convolutional neural networks’ (CNNs) and closely approached ensemble deep learning (DL) techniques’ accuracy. Originality/value: The proposed approach simplifies the process, enhances efficiency and improves interpretability compared to more complex CNNs and ensemble DL techniques.
dc.description.funderANID/FONDECYT Postdoctorado; Folio: 3230027
dc.format.extent9 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1108/ILT-12-2023-0414
dc.identifier.eissn1758-5775
dc.identifier.issn0036-8792
dc.identifier.scopusid2-s2.0-85193524385
dc.identifier.urihttps://doi.org/10.1108/ILT-12-2023-0414
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/103052
dc.identifier.wosidWOS:001226890600001
dc.information.autorucEscuela de Ingeniería; Ramteke R., Sangharatna Munneshwar; S/I; 1315678
dc.information.autorucEscuela de Ingeniería; Marian, Max; 0000-0003-2045-6649; 1247429
dc.issue.numero5
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final607
dc.pagina.inicio599
dc.revistaIndustrial Lubrication and Tribology
dc.rightsacceso restringido
dc.subjectMachine learning
dc.subjectArtificial intelligence
dc.subjectWear
dc.subjectFeature extraction
dc.subjectFeature classification
dc.subject.ddc600
dc.subject.deweyTecnologíaes_ES
dc.titleWear particle image analysis: feature extraction, selection and classification by deep and machine learning
dc.typeartículo
dc.volumen76
sipa.codpersvinculados1315678
sipa.codpersvinculados1247429
sipa.trazabilidadORCID;2025-03-03
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