@INPROCEEDINGS{Kaul2016,
  howpublished = {Conference Proceedings},
  author = {Kaul, Thorben AND Meyer, Tobias AND Sextro, Walter},
  title = {Modeling of Complex Redundancy in Technical Systems with Bayesian
	Networks},
  booktitle = {Proceedings of the Third European Conference of the Prognostics and
	Health Management Society 2016},
  year = {2016},
  abstract = {Redundancy is a common approach to improve system reliability, availability
	and safety in technical systems. It is achieved by adding functionally
	equivalent elements that enable the system to remain operational
	even though one or more of those elements fail. This paper begins
	with an overview on the various terminologies and methods for redundancy
	concepts that can be modeled sufficiently using established reliability
	analysis methods. However, these approaches yield very complex system
	models, which limits their applicability. In current research, Bayesian
	Networks (BNs), especially Dynamic Bayesian Networks (DBNs) have
	been successfully used for reliability analysis because of their
	benefits in modeling complex systems and in representing multi-state
	variables. However, these approaches lack appropriate methods to
	model all commonly used redundancy concepts. To overcome this limitation,
	three different modeling approaches based on BNs and DBNs are described
	in this paper. Addressing those approaches, the benefits and limitations
	of BNs and DBNs for modeling reliability of redundant technical systems
	are discussed and evaluated.},
  note = {Preprint: \url{http://www.tobi-meyer.de/Kaul2016preprint.pdf}}
}