Experimentation on NN Models for Hazard Identification in Machinery Functional Safety

DC ElementWertSprache
dc.contributor.authorIyenghar, Padma
dc.contributor.authorKieviet, Michael
dc.contributor.authorPulvermueller, Elke
dc.contributor.authorWuebbelmann, Juergen
dc.contributor.editorDorksen, H.
dc.contributor.editorScanzio, S.
dc.contributor.editorJasperneite, J.
dc.contributor.editorWisniewski, L.
dc.contributor.editorMan, K.F.
dc.contributor.editorSauter, T.
dc.contributor.editorSeno, L.
dc.contributor.editorTrsek, H.
dc.contributor.editorVyatkin, V.
dc.date.accessioned2024-01-04T10:29:09Z-
dc.date.available2024-01-04T10:29:09Z-
dc.date.issued2023
dc.identifier.isbn9781665493130
dc.identifier.issn1935-4576
dc.identifier.urihttp://osnascholar.ub.uni-osnabrueck.de/handle/unios/72985-
dc.descriptionCited by: 0; Conference name: 21st IEEE International Conference on Industrial Informatics, INDIN 2023; Conference date: 17 July 2023 through 20 July 2023; Conference code: 192026
dc.description.abstractThe use of Artificial Intelligence (AI) in machinery functional safety can enhance efficiency and accuracy by automating tasks previously carried out by humans. This paper presents an experimental evaluation of Neural Network (NN) models for hazard identification in machinery functional safety. The systematic study includes own implementations of NN models using open source building blocks and the use of an open source conversational AI framework with various pipeline configurations. The paper provides a comparative analysis of the qualitative and quantitative parameters for the models and configurations. © 2023 IEEE.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE International Conference on Industrial Informatics (INDIN)
dc.subjectAI
dc.subjectExperimental evaluation
dc.subjectFunctional Safety
dc.subjecthazard identification
dc.subjectHazards
dc.subjectmachinery functional safety
dc.subjectModel configuration
dc.subjectmodel configurations
dc.subjectNeural Network
dc.subjectNeural network model
dc.subjectNeural-networks
dc.subjectOpen-source
dc.subjectRasa
dc.subjectSystematic study
dc.titleExperimentation on NN Models for Hazard Identification in Machinery Functional Safety
dc.typeconference paper
dc.identifier.doi10.1109/INDIN51400.2023.10218319
dc.identifier.scopus2-s2.0-85171158300
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85171158300&doi=10.1109%2fINDIN51400.2023.10218319&partnerID=40&md5=c8ab04eb15d6b4ba86759d6c999e52b8
dc.description.volume2023-July
dcterms.isPartOf.abbreviationIEEE Int. Conf. Ind. Informatics (INDIN)
local.import.remainsaffiliations : Innotec GmbH, Erlenweg 12, Melle, 49324, Germany; Osnabrueck University of Applied Sciences, Faculty of Engineering and Computer Science, Germany; University of Osnabrueck, Software Engineering Research Group, Germany
local.import.remainscorrespondence_address : P. Iyenghar; Innotec GmbH, Melle, Erlenweg 12, 49324, Germany; email: piyengha@uos.de
local.import.remainspublication_stage : Final
crisitem.author.deptInstitut für Informatik-
crisitem.author.deptidinstitute12-
crisitem.author.parentorgFB 06 - Mathematik/Informatik/Physik-
crisitem.author.grandparentorgUniversität Osnabrück-
crisitem.author.netidPuEl525-
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