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Towards Collaborative Predictive Maintenance Leveraging Private Cross-Company Data

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dc.contributor.authorMohr, Marisa
dc.contributor.authorBecker, Christian
dc.contributor.authorMöller, Ralf
dc.contributor.authorRichter, Matthias
dc.contributor.editorReussner, Ralf H.
dc.contributor.editorKoziolek, Anne
dc.contributor.editorHeinrich, Robert
dc.date.accessioned2021-01-27T13:33:45Z
dc.date.available2021-01-27T13:33:45Z
dc.date.issued2021
dc.description.abstractThe accuracy of a predictive maintenance model is largely determined by the available training data. This puts such machine learning systems out of reach for small and medium-sized production engineering companies, as they are often unable to provide training data in sufficient quality and quantity. Building a collaborative model by pooling training data across many companies would solve this issue, but this data cannot simply be consolidated in a central location while at the same time preserving data integrity and security. This paper enables a collaborative model for predictive maintenance on cross-company data without exposing participants' business information by connecting two recent methodologies: blockchain and federated learning.en
dc.identifier.doi10.18420/inf2020_39
dc.identifier.isbn978-3-88579-701-2
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34747
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2020
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-307
dc.subjectIndustrial Internet of Things
dc.subjectMachine Learning
dc.subjectBlockchain
dc.subjectFederated Learning
dc.titleTowards Collaborative Predictive Maintenance Leveraging Private Cross-Company Dataen
gi.citation.endPage432
gi.citation.startPage427
gi.conference.date28. September - 2. Oktober 2020
gi.conference.locationKarlsruhe
gi.conference.sessiontitleKünstliche Intelligenz für kleine und mittlere Unternehmen

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