Zeitschriftenartikel:
F. Ansari, L. Kohl, J. Giner, H. Meier:
"Text mining for AI enhanced failure detection and availability optimization in production systems";
CIRP Annals-Manufacturing Technology,
70
(2021),
1;
S. 373
- 376.
Kurzfassung englisch:
The success of data-driven maintenance is strongly dependant on effective use of AI and multi-structured data sources. Introducing and integrating an AI-enhanced methodology in reliability-centred maintenance study of complex production systems leads to reducing failure rates and optimizing availability. In manufacturing enterprises, information about machine failures and expert knowledge are often stored in digital shift books (DSB). This paper introduces a transferable and scalable AI-enhanced methodology for DSB in automotive industry, which enhances Overall Equipment Efficiency (OEE) by optimizing availability through reducing the Mean Failure Detection Time (MFDT). Experimental investigations in the use-case suggest an OEE increase by over 5%.
Schlagworte:
Maintenance; Artificial intelligenc; Digital shift book
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1016/j.cirp.2021.04.045
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.