Optimizing Function-Based Fault Propagation Model Resilience Using Expected Cost Scoring

Shared by Kai Goebel, updated on Nov 26, 2018


Author(s) :
D. Hulse, C. Hoyle, K. Goebel, I. Tumer

Complex engineered systems are often associated with risk due to high failure consequences, high complexity, and large investments. As a result, it is desirable for complex engineered systems to be resilient such that they can avoid or quickly recover from faults. Ideally, this should be done at the early design stage where designers are most able to explore a large space of concepts. Previous work has shown that functional models can be used to predict fault behavior and motivate design work, however little has been done to formally optimize a design based on these predictions, partially because the effects of these models have not been quantified into an objective function to optimize. This work introduces a scoring function which integrates with a fault scenario-based simulation to allow for the optimization of functional model resilience. This scoring function enables the designer to consider trade offs between the design costs, operating costs, and expected fault response of a given design, and may be parameterized in terms of designer-specified design changes to suit optimization. This framework is adapted and applied to the optimization of controlling functions which recover flows in a monopropellant orbiter. In this case study an evolutionary algorithm is found to find the optimal logic for these functions, showing an improvement over a typical a-priori guess by exploring a large range of solutions, demonstrating the value of the approach

show more info
Publication Name
Publication Location
Year Published



Add New Comment