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Towards Collaborative Predictive Maintenance Leveraging Private Cross-Company Data

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2021

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Gesellschaft für Informatik, Bonn

Zusammenfassung

The 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.

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Mohr, Marisa; Becker, Christian; Möller, Ralf; Richter, Matthias (2021): Towards Collaborative Predictive Maintenance Leveraging Private Cross-Company Data. INFORMATIK 2020. DOI: 10.18420/inf2020_39. Gesellschaft für Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-701-2. pp. 427-432. Künstliche Intelligenz für kleine und mittlere Unternehmen. Karlsruhe. 28. September - 2. Oktober 2020

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