Automated Fault Tree Generation from Requirement Structures
The increasing complexity of today’s vehicles gives drivers help with
everything from adaptive cruise control to warning lights for low fuel
level. But the increasing functionality also increases the risk of
failures in the system. To prevent system failures, different safety
analytic methods can be used, e.g., fault trees and/or
FMEA-tables. These methods are generally performed manually, and due
to the growing system size the time spent on safety analysis is
growing with increased risk of human errors. If the safety analysis
can be automated, lots of time can be saved.
This thesis investigates the possibility to generate fault trees from
safety requirements as well as which additional information, if any,
that is needed for the generation. Safety requirements are
requirements on the systems functionality that has to be fulfilled for
the safety of the system to be guaranteed. This means that the safety
of the truck, the driver, and the surroundings, depend on the
fulfillment of those requirements. The requirements describing the
system are structured in a graph using contract theory. Contract
theory defines the dependencies between requirements and connects them
in a contract structure.
To be able to automatically generate the fault tree for a system,
information about the system’s failure propagation is needed. For this
a Bayesian network is used. The network is built from the contract
structure and stores the propagation information in all the nodes of
the network. This will result in a failure propagation network, which
the fault tree generation will be generated from. The failure
propagation network is used to see which combinations of faults in the
system can violate the safety goal, i.e., causing one or several
hazards. The result of this will be the base of the fault tree.
The automatic generation was tested on two different Scania systems,
the fuel level display and the dual circuit steering. Validation was
done by comparing the automatically generated trees with manually
generated trees for the two systems showing that the proposed method
works as intended. The case studies show that the automated fault tree
generation works if the failure propagation information exists and can
save a lot of time and also minimize the errors made by manually
generating the fault trees. The generated fault trees can also be used
to validate written requirements to by analyzing the fault trees
created from them.
Johan Andersson
2015
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Last updated: 2021-11-10
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