Modeling of Complex Redundancy in Technical Systems with Bayesian Networks

Thorben Kaul, Tobias Meyer, and Walter Sextro
Submission Type: 
Full Paper
phmec_16_006.pdf262.79 KBJune 22, 2016 - 1:38am

Modeling of Complex Redundancy in Technical Systems with Bayesian Networks

Thorben Kaul, Tobias Meyer , Walter Sextro

University of Paderborn, Faculty of Mechanical Engineering, Mechatronics and Dynamics, 33098 Paderborn, Germany
thorben.kaul|tobias.meyer|[email protected]


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, e.g.\ Reliability Block Diagrams (RBDs), Dynamic Fault Trees (DFTs) or Markov Chains (MCs).
However, these approaches yield very complex system models and are limited in applicability by this.
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 techniques based on BNs and DBNs are described in this work.
Addressing those approaches, the benefits and limitations of BNs and DBNs for modeling reliability of redundant technical systems are discussed and evaluated.

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Submission Keywords: 
Bayesian network
Markov chain
Submission Topic Areas: 
Modeling and simulation
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