Talks and Poster Presentations (with Proceedings-Entry):
M. Steinegger, M. Melik-Merkumians, J. Zajc, G. Schitter:
"A Framework for Automatic Knowledge-Based Fault Detection in Industrial Conveyor Systems";
Talk: 22nd IEEE International Conference on Emerging Technologies And Factory Automation,
- 09-15-2017; in: "Proceedings on 22nd IEEE International Conference on Emerging Technologies And Factory Automation",
Paper ID 222,
In this paper, a framework for automatic generation
of a flexible and modular system for fault detection and diagnosis
(FDD) is proposed. The method is based on an ontology-based integration
framework, which gathers the information from various
engineering artifacts. Based on the ontologies, FDD functions are
generated based on structural and procedural generation rules.
The rules are encoded as SPARQL queries which automatically
build logical segments of the entire manufacturing system in
the ontology, assign sub-processes to these segments, and finally
generate the appropriate FDD system for the sub-process. These
generated modular FDD functions are additionally combined in
a modular way to enable the fault detection and diagnosis of the
entire system. The effectiveness of the approach is demonstrated
by a first application to a conveyor system.
Knowledge, Ontology, Code Generation, Fault Detection
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.