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https://dspace.lboro.ac.uk/2134/14348
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Title: | An investigation into hazard-centric analysis of complex autonomous systems |
Authors: | Downes, C.G. |
Keywords: | Hazards Certification Autonomous systems Systems engineering Behavioural modelling Bayesian belief |
Issue Date: | 2013 |
Publisher: | © Clive George Downes |
Abstract: | This thesis proposes a hypothesis that a conventional, and essentially manual, HAZOP process can be
improved with information obtained with model-based dynamic simulation, using a Monte Carlo
approach, to update a Bayesian Belief model representing the expected relations between cause and
effects – and thereby produce an enhanced HAZOP. The work considers how the expertise of a
hazard and operability study team might be augmented with access to behavioural models,
simulations and belief inference models. This incorporates models of dynamically complex system
behaviour, considering where these might contribute to the expertise of a hazard and operability study
team, and how these might bolster trust in the portrayal of system behaviour. With a questionnaire
containing behavioural outputs from a representative systems model, responses were collected from a
group with relevant domain expertise. From this it is argued that the quality of analysis is dependent
upon the experience and expertise of the participants but this might be artificially augmented using
probabilistic data derived from a system dynamics model. Consequently, Monte Carlo simulations of
an improved exemplar system dynamics model are used to condition a behavioural inference model
and also to generate measures of emergence associated with the deviation parameter used in the study.
A Bayesian approach towards probability is adopted where particular events and combinations of
circumstances are effectively unique or hypothetical, and perhaps irreproducible in practice.
Therefore, it is shown that a Bayesian model, representing beliefs expressed in a hazard and
operability study, conditioned by the likely occurrence of flaw events causing specific deviant
behaviour from evidence observed in the system dynamical behaviour, may combine intuitive
estimates based upon experience and expertise, with quantitative statistical information representing
plausible evidence of safety constraint violation. A further behavioural measure identifies potential
emergent behaviour by way of a Lyapunov Exponent. Together these improvements enhance the
awareness of potential hazard cases. |
Description: | A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University. |
Sponsor: | EPSRC, BAE SYSTEMS |
URI: | https://dspace.lboro.ac.uk/2134/14348 |
Appears in Collections: | PhD Theses (Computer Science)
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