Modern spacecraft, aircraft, and surface vehicles support complex science missions in harsh environments. These spacecraft and vehicles provide diverse functionality that is deployed on increasingly complex and heterogeneous hardware and mechanical systems, with stringent dependability requirements.
The technical feasibility of constructing a constraint-checking system for fault management (FM) trade space exploration was examined to provide rigorous performance guarantees for FM strategies for complex cyber-physical systems. The innovation is a means to address FM decisions (e.g., sensor placement) that require multi-dimensional optimizations to maximize sensitivity and specificity to achieve dependability, availability, and integrity guarantees, while minimizing factors such as energy expenditure, risk, latency, and cost. The Architecture Framework for Fault Management Assessment and Design (AFFMAD) will assist FM engineers’ early evaluation of FM strategies and improve the efficiency of implementing and testing those strategies.
AFFMAD provides a systematic framework for FM trade space exploration for complex, mission-critical cyber-physical systems during concept development early in a system design lifecycle. The process starts with the user specifying FM performance goals and an FM trade space in an existing multidomain model integration framework. As the FM strategy is refined concurrently with system formulation, AFFMAD will gather appropriate constraints from the domain model and translate them into a Constraint Satisfaction Problem (CSP), which will then be submitted to a CSP solver. AFFMAD will then report violated constraints.
The key innovation is to create FM models in a form that supports iteration over alternative FM strategies in order to optimize overall mission success. This is accomplished by building abstract FM cyber-physical models, annotating them with both FM characteristics and a variety of mission costs [e.g., size, weight, and power (SWAP), latency, throughput]. Those model alternates are captured in such a manner as to facilitate extraction by external tools, which can then systematically iterate over the alternatives to explore the state space of possible FM strategies. At each iteration, AFFMAD collects performance and cost information about each alternative in the context of the entire mission, as well as detailed mission phases.