Simple item page
 

Application of continuous wavelet transform and convolutional neural networks for diagnostics of screw wear in wheat extrusion

cris.virtual.author-orcid0000-0003-4811-005X
cris.virtual.author-orcid0000-0002-8897-6459
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid0000-0001-7223-6491
cris.virtualsource.author-orcidebe065e1-88e3-4328-92a9-62cb28d0570e
cris.virtualsource.author-orcidd0f13f67-14d4-453a-9b21-2771d083450d
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid110d6c25-5395-4f20-b8e7-5e160c853b52
dc.abstract.enThis study presents a hybrid diagnostic approach combining the Continuous Wavelet Transform (CWT) and Convolutional Neural Networks (CNN) for assessing screw wear in a single-screw extruder operating under controlled conditions. Electrical current signals from the drive motor were analyzed to identify changes associated with the degradation of working components. CWT scalograms were used as time–frequency inputs for a CNN classifier, achieving a classification accuracy of 92.3% in distinguishing between new and worn screw states. Principal Component Analysis (PCA) confirmed clear separability of operating conditions, with the first two components explaining over 99% of the total variance. The results indicate that electrical signals contain diagnostically relevant information and that their combined analysis using CWT and CNN enables automated, non-invasive condition assessment with potential applicability in predictive maintenance systems without additional sensors.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorDurczak, Karol
dc.contributor.authorWitaszek, Kamil
dc.contributor.authorMarkowski, Piotr
dc.contributor.authorDudnyk, Alla
dc.contributor.authorRybacki, Piotr
dc.date.access2026-03-06
dc.date.accessioned2026-03-06T09:27:08Z
dc.date.available2026-03-06T09:27:08Z
dc.date.copyright2026-02-18
dc.date.issued2026
dc.description.abstract<jats:p>This study presents a hybrid diagnostic approach combining the Continuous Wavelet Transform (CWT) and Convolutional Neural Networks (CNN) for assessing screw wear in a single-screw extruder operating under controlled conditions. Electrical current signals from the drive motor were analyzed to identify changes associated with the degradation of working components. CWT scalograms were used as time–frequency inputs for a CNN classifier, achieving a classification accuracy of 92.3% in distinguishing between new and worn screw states. Principal Component Analysis (PCA) confirmed clear separability of operating conditions, with the first two components explaining over 99% of the total variance. The results indicate that electrical signals contain diagnostically relevant information and that their combined analysis using CWT and CNN enables automated, non-invasive condition assessment with potential applicability in predictive maintenance systems without additional sensors.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,1
dc.description.number3
dc.description.points140
dc.description.versionfinal_published
dc.description.volume28
dc.identifier.doi10.17531/ein/217552
dc.identifier.eissn2956-3860
dc.identifier.issn1507-2711
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/7656
dc.identifier.weblinkhttps://ein.org.pl/Application-of-Continuous-Wavelet-Transform-and-Convolutional-Neural-Networks-for,217552,0,2.html
dc.languageen
dc.pbn.affiliationmechanical engineering
dc.relation.ispartofEksploatacja i Niezawodnosc
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.en"predictive diagnostics
dc.subject.enscrew extruder
dc.subject.enCWT
dc.subject.enCNN
dc.subject.endeep learning
dc.subject.ensignal analysis"
dc.subtypeArticleEarlyAccess
dc.titleApplication of continuous wavelet transform and convolutional neural networks for diagnostics of screw wear in wheat extrusion
dc.typeJournalArticle
dspace.entity.typePublication