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dc.contributor.authorSchmid, P. A. E.
dc.date.accessioned2021-12-09T14:04:00Z
dc.date.available2021-12-09T14:04:00Z
dc.date.issued2019
dc.identifier.citationCIO Applications urope, July 2019, 03, 2019, pp. 29-30.
dc.identifier.urihttps://yoda.csem.ch/handle/20.500.12839/361
dc.description.abstractReliably predicting the place, time and strength of an earthquake - this wish is probably as old as mankind itself. Every machine manufacturer and operator of a plant also wants a reliable prognosis of the end of its service life. The aim is to ensure long-term operation, detect errors at an early stage and prevent failures. In predictive maintenance, the focus is on identifying signs of random failures at an early stage and predicting ageing processes. Therefore maintenance can be planned in good time and the risk of failures and downtime can be minimized.
dc.titleMachines with Brain – Predictive Maintenance with Deep Neural Networks
dc.typeJournal Article
dc.type.csemdivisionsDiv-R
dc.type.csemresearchareasIndustry 4.0
dc.identifier.urlhttps://rpa.cioapplicationseurope.com/cxoinsights/machines-with-brain-predictive-maintenance-with-deep-neural-networks-nid-1300.html


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