Engineering topics
Publications and source records attributed to Karsai, G..
This presentation describes how assurance arguments for the radiation reliability of a system can be modeled in Goal Structuring Notation over the life-cycle of a project. A practical implementation of the NASA-STD-8729.1 and Model-Based Mission Assurance.
A Goal Structuring Notation (GSN) model is presented to argue for the radiation reliability of a SEU SCRAM experiment in a CubeSat form-factor for a 1 year old polar LEO mission.
A systematic approach is presented to constructing a radiation assurance case using Goal Structuring Notation (GSN) for spacecraft containing COTS parts. The GSN paradigm is applied to an SRAM single-event upset experiment board designed to fly on a CubeSat in January 2017. A custom software language for development of a GSN assurance case is under development at Vanderbilt. Construction of a radiation assurance case without use of hardened parts or extensive radiation testing is discussed.
The attached presentation is a summary of how mission assurance is supported by model-based representations of spacecraft systems that can define sub-system functionality and interfacing, reliability parameters, as well as detailing a new paradigm for assurance, a model-centric and not document-centric process.
A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.
Graphical simulation is a cost-effective solution for developing and testing robots and their control systems. The availability of various high-performance workstations makes these systems feasible. Simulation offers preliminary testing of systems before their actual realizations, and it provides a framework for developing new control and planning algorithms. On the other hand, these simulation systems have to have the capability of incorporating various knowledge-based system components, e.g., task planners, representation formalisms, etc. They also should have an appropriate user interface, which makes possible the creation and control of simulation models. ROBOSIM was developed jointly by MSFC and Vanderbilt University, first in a VAX environment. Recently, the system has been ported to an HP-9000 workstation equipped with an SRX graphics accelerator. The user interface of the system now contains a menu- and icon-based facility, as well as the original ROBOSIM language. The system is also coupled to a symbolic computing system based on Common Lisp, where knowledge-based functionalities are implemented. The knowledge-based layer uses various representation and reasoning facilities for programming and testing the control systems of robots.
A programming model and architecture which was developed for the design and implementation of complex, heterogeneous measurement and control systems is described. The Multigraph Architecture integrates artificial intelligence techniques with conventional software technologies, offers a unified framework for distributed and shared memory based parallel computational models and supports multiple programming paradigms. The system can be implemented on different hardware architectures and can be adapted to strongly different applications.