Reference:
K. Verbert,
B. De Schutter, and
R. Babuska,
"Reasoning under uncertainty for knowledge-based fault diagnosis: A
comparative study," Proceedings of the 9th IFAC Symposium on Fault
Detection, Supervision and Safety of Technical Processes (SafeProcess
2015), Paris, France, pp. 422-427, Sept. 2015.
Abstract:
This paper addresses reasoning under uncertainty for knowledge-based
fault diagnosis. We illustrate how the fault diagnosis task is
influenced by uncertainty. Furthermore, we compare how the diagnosis
task is solved in the Bayesian and the Dempster-Shafer reasoning
framework, in terms of both diagnostic performance and additional
objectives, like transparency, adaptability, and computational
efficiency. Since the diagnosis problem is influenced by different
kinds of uncertainty, it is not straightforward to determine the
optimal reasoning method. First, the different uncertain influences
all have their own characteristics, asking for different reasoning
approaches. So, to solve the whole problem in one reasoning framework,
approximations and trade-offs need to be made. Second, which types of
uncertainty are present and to what extent, is highly
application-specific. Therefore, the best framework can only be
assigned after the problem, the uncertainty characteristics, and the
user requirements are known.