Large-Scale Fault Diagnosis
for On-Board Train Systems

B.D. Netten1 , R.A. Vingerhoeds1,2

1	Delft University of Technology, Faculty of Technical Mathematics and Informatics,
Knowledge Based Systems Group, Julianalaan 132, 2628 BL Delft, The Netherlands.
E-mail: bart@kgs.twi. tudelft.nl, rob@kgs.twi.tudelft.nl
2	University of Wales Swansea, Department of Electrical and Electronic Engineering,
Singleton Park, Swansea SA2 8PP, United Kingdom.


Abstract
A new approach is developed for fault diagnosis during different stages of
development and operation of large train systems, incorporating case-based
reasoning, conditional probabilities and indexing networks. Due to the size and
complexity, the explicit, complete and accurate modelling of the on-board train
systems is regarded impossible. The knowledge is implicitly available in fault-cases
with possible symptoms, test results and actions. Off-line, different diagnostic
systems are automatically maintained and (re)generated. Knowledge and experience
of manufacturers and railway companies are fed back into all systems, but only after
validation by authorised personnel. On-line, the system responses are consistent and
fast enough, despite the size and uncertainty in the fault-cases. Available case-based
reasoning tools have serious limitations in permissible size of the problem, handling
probability factors, meeting required response times and satisfying the real-time
requirements. The novelty of the proposed approach is that fault-networks, rather
than fault-trees, are built automatically as the indexing structure of the case-base for
on-line use.
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