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Beiträge in Tagungsbänden:

F. Ansari, R. Glawar, W. Sihn:
"Prescriptive Maintenance of CPPS by Integrating Multimodal Data with Dynamic Bayesian Networks";
in: "Machine Learning for Cyber Physical Systems", 11; J. Beyerer, A. Maier, O. Niggemann (Hrg.); Springer Vieweg, Berlin, Heidelberg, 2020, ISBN: 978-3-662-59084-3, S. 1 - 8.



Kurzfassung englisch:
The complexity and data-driven characteristics of Cyber Physical Production
Systems (CPPS) impose new requirements on maintenance strategies and
models. Maintenance in the era of Industry 4.0 should, therefore, advances prediction,
adaptation and optimization capabilities in horizontally and vertically integrated
CPPS environment. This paper contributes to the literature on
knowledge-based maintenance by providing a new model of prescriptive maintenance,
which should ultimately answer the two key questions of "what will happen?
and "how can we make it happen?", in addition to "what happened?" and
"why did it happen?". In this context, we intend to go beyond the scope of the
research project "Maintenance 4.0" by i) proposing a data-model considering
multimodalities and structural heterogeneities of maintenance records, and ii)
providing a methodology for integrating the data-model with Dynamic Bayesian
Network (DBN) for the purpose of learning cause-effect relations, predicting future
events, and providing prescriptions for improving maintenance planning.

Schlagworte:
Maintenance, CPPS, Prescriptive Analytics, Cause-Effect Analysis, Data Model, Bayesian Network


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1007/978-3-662-59084-3


Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.