Companies are increasingly challenged by the impending loss of knowledge due to demographic change and employee loss. In times of advancing digitalization, it is
important to make large datasets accessible and usable, aiming at increasing resource efficiency within the company on the one hand and being able to offer customers additional services on the other. Given the background of implementing efficient quality management and predictive maintenance with the same system, technological key
figures and process control must first be determined. In the field of intelligent maintenance, however, it is not always possible to record error states of physical systems in
series operation as a data set. Deliberately allowing faults to occur under real production conditions could lead to fatal failures or even the destruction of the system.
The targeted generation of faults under highly controlled conditions can also be timeconsuming, cost-intensive, or even impractical.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:88309 |
| Date | 28 November 2023 |
| Creators | Reuter, Thomas, Massalsky, Kristin, Burkhardt, Thomas |
| Publisher | Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 16, urn:nbn:de:bsz:14-qucosa2-882872, qucosa:88287, urn:nbn:de:bsz:14-qucosa2-883085, qucosa:88308 |
Page generated in 0.045 seconds