For many complex engineered systems, a risk informed approach to design is critical to ensure both robust safety and system reliability. Early identification of failure paths in complex systems can greatly reduce the costs and risks absorbed by a project in future failure mitigation strategies. By exploring the functional effect of potential failures, designers can identify preferred architectures and technologies prior to acquiring specific knowledge of detailed physical system forms and behaviors. Early design-stage failure analysis is enabled by model-based design, with several research methodologies having been developed to support this design stage analysis through the use of computational models. The abstraction necessary for implementation at the design stage, however, leads to challenges in validating the analysis results presented by these models.
This paper describes initial work on the comparison of models at varying levels of abstraction with results obtained on an experimental testbed in an effort to validate a function-based failure analysis method. Specifically, the potential functional losses of a simple rover vehicle are compared with experimental findings of similar failure scenarios. Expected results of the validation procedure suggest that a model’s validity and quality are a function of the depth to which functional details are described.