Beiträge in Tagungsbänden:

K. Kovacs, F. Ansari, R. Glawar, W. Sihn et al.:
"A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance";
in: "Machine Learning for Cyber Physical Systems, Technologien für die intelligente Automation", 9; J. Beyerer, C. Kühnert et al. (Hrg.); Springer Vieweg, Berlin, 2019, ISBN: 978-3-662-58485-9, 10 S.

Kurzfassung englisch:
Digital transformation and evolution of integrated compu-tational and visualisation technologies lead to new op-portunities for reinforcing knowledge-based maintenance through collection, processing and provision of actiona-ble information and recom-mendations for maintenance op-erators. 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. Select-ing appropriate digital assistance systems (DAS), howev-er, highly depends on hardware and IT infrastructure, software and interfaces as well as information provi-sion 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, underly-ing machine learning algorithms deployed by each product could provide certain level of intelligence and ultimate-ly could transform diagnostic maintenance capabilities into predictive and prescriptive maintenance. This paper proposes a pro-cess-based model to facilitate the selec-tion of suitable DAS for supporting mainte-nance opera-tions in manufacturing industries. This solution is em-ployed for a struc-tured requirement elicitation from various application domains and ultimately map-ping the requirements to existing digital assistance solutions. Using the proposed ap-proach, a (combination of) digital assistance system is selected and linked to mainte-nance 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 im-prove the quality of recommenda-tions and to identify the strengths and weaknesses of each system in association to practical use-cases from TU Wien Pilot-Factory In-dustry 4.0.

Maintenance, Digital Assistance Systems, Process Model, Industry 4.0

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

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