Towards Design Principles for a Real-Time Anomaly Detection Algorithm Benchmark Suited to Industrie 4.0 Streaming Data

DC FieldValueLanguage
dc.contributor.authorStahmann, Philip
dc.contributor.authorRieger, Bodo
dc.contributor.editorBui, T.X.
dc.date.accessioned2023-07-12T06:59:33Z-
dc.date.available2023-07-12T06:59:33Z-
dc.date.issued2022
dc.identifier.isbn9780998133157
dc.identifier.issn1530-1605
dc.identifier.urihttp://osnascholar.ub.uni-osnabrueck.de/handle/unios/72122-
dc.descriptionCited by: 2; Conference name: 55th Annual Hawaii International Conference on System Sciences, HICSS 2022; Conference date: 3 January 2022 through 7 January 2022; Conference code: 187534
dc.description.abstractThe vision of Industrie 4.0 includes the automated reduction of anomalies in flexibly combined production machine groups up to a zero-failure ideal. Algorithmic real-time detection of production anomalies may build the basis for machine self-diagnosis and self-repair during production. Several real-time anomaly detection algorithms appeared in recent years. However, different algorithms applied to the same data may result in contradictory detections. Thus, real-time anomaly detection in Industrie 4.0 machine groups may require a benchmark ranking for algorithms to increase detection results' reliability. This paper makes a qualitative research contribution based on ten expert interviews to find design principles for such a benchmark ranking. The experts were interviewed on three categories, namely timeliness, thresholds and qualitative classification. The study's results can be used as groundwork for a prototypical implementation of a benchmark. © 2022 IEEE Computer Society. All rights reserved.
dc.language.isoen
dc.publisherIEEE Computer Society
dc.relation.ispartofProceedings of the Annual Hawaii International Conference on System Sciences
dc.subjectSignal detection
dc.subject% reductions
dc.subjectAlgorithmics
dc.subjectAnomaly-detection algorithms
dc.subjectDesign Principles
dc.subjectProduction-machines
dc.subjectReal-time anomaly detections
dc.subjectReal-time detection
dc.subjectSelf repair
dc.subjectSelf-diagnose
dc.subjectStreaming data
dc.subjectAnomaly detection
dc.titleTowards Design Principles for a Real-Time Anomaly Detection Algorithm Benchmark Suited to Industrie 4.0 Streaming Data
dc.typeconference paper
dc.identifier.scopus2-s2.0-85152137348
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85152137348&partnerID=40&md5=df80fc0d771e16f855c6a8c1e1e59256
dc.description.volume2022-January
dc.description.startpage6323 – 6329
dcterms.isPartOf.abbreviationProc. Annu. Hawaii Int. Conf. Syst. Sci.
local.import.remainsaffiliations : University of Osnabrueck, Germany
local.import.remainspublication_stage : Final
crisitem.author.deptFB 09 - Wirtschaftswissenschaften-
crisitem.author.deptidfb09-
crisitem.author.parentorgUniversität Osnabrück-
crisitem.author.netidRiBo787-
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