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Automatic fault tree construction for railway safety systems
thesisposted on 2010-11-18, 12:49 authored by Jason Henry
This thesis looks into ways of developing a new approach for fault tree automation. Initially an extensive literature survey was undertaken to try and identify if any methods contained useful features that might warrant further development. The two methods that were chosen were the decision table method and the digraph method. The new hybrid method is based on the flexibility of the decision table method but incorporates a way of detecting, classifying and analysing control loops, similar to the use of operators in the digraph approach. As well as using operators to deal with control loops new operators are introduced that deal with current and no current in electrical circuits. These new operators have been developed to be able to handle components that are common to multiple circuits. The advantages of applying operators during fault tree construction is to reduce the number of repeated and inconsistent events that may occur in the tree, and to significantly reduced the size of the constructed fault tree. Thus producing a tree logic that can easily be followed by an analyst and is in an appropriate format for direct input to an fault tree analysis code. The new method has been automated and successfully applied to three railway safety systems obtained from London Underground Ltd. To test the ability of the Automatic Fault Tree Construction Code (AFTCC) the complexity of each of the three systems increased. The first system, the Train Stop system, did not contain any circuits; the second, the Train Detection system, contained simple circuits and lastly the Train Braking system, contained multiple nested circuits.
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering
Publisher© Jason Henry
NotesA Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.
EThOS Persistent IDuk.bl.ethos.321239