System of Systems Hazard Analysis Using Simulation and Machine Learning

  • Robert Alexander
  • Dimitar Kazakov
  • Tim Kelly
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4166)


In the operation of safety-critical systems, the sequences by which failures can lead to accidents can be many and complex. This is particularly true for the emerging class of systems known as systems of systems, as they are composed of many distributed, heterogenous and autonomous components. Performing hazard analysis on such systems is challenging, in part because it is difficult to know in advance which of the many observable or measurable features of the system are important for maintaining system safety. Hence there is a need for effective techniques to find causal relationships within these systems. This paper explores the use of machine learning techniques to extract potential causal relationships from simulation models. This is illustrated with a case study of a military system of systems.


Hazard Analysis Potential Causal Relationship Normal Accident Hospital Evacuation Guide Word 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Robert Alexander
    • 1
  • Dimitar Kazakov
    • 1
  • Tim Kelly
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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