Vorträge und Posterpräsentationen (ohne Tagungsband-Eintrag):

K. Kovacs, F. Ansari, R. Glawar, W. Sihn et al.:
"A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance";
Vortrag: ML4CPS 2018 - Machine Learning for Cyber Physical Systems, Karlsruhe; 23.10.2018 - 24.10.2018.

Kurzfassung englisch:
Digital transformation and evolution of integrated computational and visualisation
technologies lead to new opportunities for reinforcing knowledge-based
maintenance through collection, processing and provision of actionable information
and recommendations for maintenance operators. Providing actionable information regarding
both corrective and preventive maintenance activities at the right time may lead
to reduce human failure and improve overall efficiency within maintenance processes.
Selecting appropriate digital assistance systems (DAS), however, highly depends on
hardware and IT infrastructure, software and interfaces as well as information provision
methods such as visualization. The selection procedures can be challenging due to the
wide range of services and products available on the market. In particular, underlying
machine learning algorithms deployed by each product could provide certain level of
intelligence and ultimately could transform diagnostic maintenance capabilities into
predictive and prescriptive maintenance. This paper proposes a process-based model to
facilitate the selection of suitable DAS for supporting maintenance operations in manufacturing
industries. This solution is employed for a structured requirement elicitation
from various application domains and ultimately mapping the requirements to existing
digital assistance solutions. Using the proposed approach, a (combination of) digital
assistance system is selected and linked to maintenance activities. For this purpose, we
gain benefit from an in-house process modeling tool utilized for identifying and relating
sequence of maintenance activities. Finally, we collect feedback through employing the
selected digital assistance system to improve the quality of recommendations and to
identify the strengths and weaknesses of each system in association to practical usecases
from TU Wien Pilot-Factory Industry 4.0.

Maintenance, Digital Assistance Systems, Process Model, Industry 4.0.

"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)

Elektronische Version der Publikation:

Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.