A Benchmark for Real-Time Anomaly Detection Algorithms Applied in Industry 4.0
DC Element | Wert | Sprache |
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dc.contributor.author | Stahmann, Philip | |
dc.contributor.author | Rieger, Bodo | |
dc.contributor.editor | Nicosia, G. | |
dc.contributor.editor | Giuffrida, G. | |
dc.contributor.editor | Ojha, V. | |
dc.contributor.editor | La Malfa, E. | |
dc.contributor.editor | La Malfa, G. | |
dc.contributor.editor | Pardalos, P. | |
dc.contributor.editor | Di Fatta, G. | |
dc.contributor.editor | Umeton, R. | |
dc.date.accessioned | 2023-07-12T06:59:29Z | - |
dc.date.available | 2023-07-12T06:59:29Z | - |
dc.date.issued | 2023 | |
dc.identifier.isbn | 9783031255984 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://osnascholar.ub.uni-osnabrueck.de/handle/unios/72094 | - |
dc.description | Cited by: 0; Conference name: 8th International Conference on Machine Learning, Optimization, and Data Science, LOD 2022, held in conjunction with the 2nd Advanced Course and Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022; Conference date: 18 September 2022 through 22 September 2022; Conference code: 291259 | |
dc.description.abstract | Industry 4.0 describes flexibly combinable production machines enabling efficient fulfillment of individual requirements. Timely and automated anomaly recognition by means of machine self-diagnosis might support efficiency. Various algorithms have been developed in recent years to detect anomalies in data streams. Due to their diverse functionality, the application of different real-time anomaly detection algorithms to the same data stream may lead to different results. Existing algorithms as well as mechanisms for their evaluation and selection are context-independent and not suited to industry 4.0 settings. In this research paper, an industry 4.0 specific benchmark for real-time anomaly detection algorithms is developed on the basis of six design principles in the categories timeliness, threshold setting and qualitative classification. Given context-specific input parameters, the benchmark ranks algorithms according to their suitability for real-time anomaly detection in production datasets. The application of the benchmark is demonstrated and evaluated on the basis of two case studies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. | |
dc.language.iso | en | |
dc.publisher | Springer Science and Business Media Deutschland GmbH | |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.subject | Algorithm benchmark | |
dc.subject | Anomaly detection | |
dc.subject | Anomaly recognition | |
dc.subject | Anomaly-detection algorithms | |
dc.subject | Benchmarking | |
dc.subject | Chemical detection | |
dc.subject | Data stream | |
dc.subject | Industry 4.0 | |
dc.subject | Petroleum reservoir evaluation | |
dc.subject | Production-machines | |
dc.subject | Real time analysis | |
dc.subject | Real-time analysis | |
dc.subject | Real-time anomaly detections | |
dc.subject | Self-diagnose | |
dc.subject | Signal detection | |
dc.subject | Streaming analytic | |
dc.subject | Streaming analytics | |
dc.title | A Benchmark for Real-Time Anomaly Detection Algorithms Applied in Industry 4.0 | |
dc.type | conference paper | |
dc.identifier.doi | 10.1007/978-3-031-25599-1_3 | |
dc.identifier.scopus | 2-s2.0-85151059619 | |
dc.identifier.url | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151059619&doi=10.1007%2f978-3-031-25599-1_3&partnerID=40&md5=4a0333dd84784575ad385a8a84461596 | |
dc.description.volume | 13810 LNCS | |
dc.description.startpage | 20 – 34 | |
dcterms.isPartOf.abbreviation | Lect. Notes Comput. Sci. | |
local.import.remains | affiliations : University of Osnabrueck, Osnabrueck, Germany | |
local.import.remains | correspondence_address : P. Stahmann; University of Osnabrueck, Osnabrueck, Germany; email: pstahmann@uni-osnabrueck.de | |
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 | - |
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geprüft am 17.05.2024