DC Field | Value | Language |
dc.contributor.author | Stahmann, Philip | |
dc.contributor.author | Rieger, Bodo | |
dc.contributor.editor | Bui, T.X. | |
dc.date.accessioned | 2023-07-12T06:59:33Z | - |
dc.date.available | 2023-07-12T06:59:33Z | - |
dc.date.issued | 2022 | |
dc.identifier.isbn | 9780998133157 | |
dc.identifier.issn | 1530-1605 | |
dc.identifier.uri | http://osnascholar.ub.uni-osnabrueck.de/handle/unios/72122 | - |
dc.description | Cited 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.abstract | The 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.iso | en | |
dc.publisher | IEEE Computer Society | |
dc.relation.ispartof | Proceedings of the Annual Hawaii International Conference on System Sciences | |
dc.subject | Signal detection | |
dc.subject | % reductions | |
dc.subject | Algorithmics | |
dc.subject | Anomaly-detection algorithms | |
dc.subject | Design Principles | |
dc.subject | Production-machines | |
dc.subject | Real-time anomaly detections | |
dc.subject | Real-time detection | |
dc.subject | Self repair | |
dc.subject | Self-diagnose | |
dc.subject | Streaming data | |
dc.subject | Anomaly detection | |
dc.title | Towards Design Principles for a Real-Time Anomaly Detection Algorithm Benchmark Suited to Industrie 4.0 Streaming Data | |
dc.type | conference paper | |
dc.identifier.scopus | 2-s2.0-85152137348 | |
dc.identifier.url | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152137348&partnerID=40&md5=df80fc0d771e16f855c6a8c1e1e59256 | |
dc.description.volume | 2022-January | |
dc.description.startpage | 6323 – 6329 | |
dcterms.isPartOf.abbreviation | Proc. Annu. Hawaii Int. Conf. Syst. Sci. | |
local.import.remains | affiliations : University of Osnabrueck, Germany | |
local.import.remains | publication_stage : Final | |
crisitem.author.dept | FB 09 - Wirtschaftswissenschaften | - |
crisitem.author.deptid | fb09 | - |
crisitem.author.parentorg | Universität Osnabrück | - |
crisitem.author.netid | RiBo787 | - |