%0 Thesis %1 lukas2024uncertainty %A Lukas, Martin %B Beiträge zum Stuttgarter Maschinenbau %C Stuttgart %D 2024 %I Fraunhofer Verlag %K %N 25 %R 10.18419/opus-14106 %T Uncertainty PERMEATED - explainable AI in a condition monitoring framework for industrial assets %@ 978-3-8396-1990-2

@phdthesis{lukas2024uncertainty, added-at = {2024-04-26T15:02:40.000+0200}, address = {Stuttgart}, author = {Lukas, Martin}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2515c9935a45eb0e403ab50b12da6f370/unibiblio-3}, doi = {10.18419/opus-14106}, eventdate = {2023-12-05}, interhash = {b00b5097ce061aaa83bf956cb2d2ca7c}, intrahash = {515c9935a45eb0e403ab50b12da6f370}, isbn = {978-3-8396-1990-2}, keywords = {}, language = {eng}, number = 25, publisher = {Fraunhofer Verlag}, school = {Universität Stuttgart}, series = {Beiträge zum Stuttgarter Maschinenbau}, supervisor = {Verl, Alexander}, supervisorgnd = {173154395}, timestamp = {2024-04-26T13:02:40.000+0200}, title = {Uncertainty PERMEATED - explainable AI in a condition monitoring framework for industrial assets}, type = {Dissertation}, year = 2024 }