Graph-based real-time fault diagnostics

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

Methodology for testing and validating knowledge bases

A test and validation toolset developed for artificial intelligence programs is described. The basic premises of this method are: (1) knowledge bases have a strongly declarative character and represent mostly structural information about different domains, (2) the conditions for integrity, consistency, and correctness can be transformed into structural properties of knowledge bases, and (3) structural information and structural properties can be uniformly represented by graphs and checked by graph algorithms. The interactive test and validation environment have been implemented on a SUN workstation

Graphic simualtion test bed for robotics applications in a workstation environment

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

Multi-Domain Surety Modeling and Analysis for High Assurance Systems

Engineering systems are becoming increasingly complex as state of the art technologies am incorporated into designs. Surety modeling and analysis is an emerging science that permits an engineer to qualitatively and quantitatively predict and assess the completeness and predictability of a design. Surety is a term often used in the Department of Defense (DoD) and Department of Energy (DOE) communities, which refers to the integration of safety, security, reliability and performance aspects of design. Current risk assessment technologies for analyzing complex systems fail to adequately describe the problem, thus making assessment fragmented and non-integrated. To address this problem, we have developed a methodology and extensible software tool set to address model integration and complexity for high consequence systems. The MultiGraph Architecture (MGA) facilitates multi-domain, model-integrated modeling and analyses of complex, high-assurance systems. The MGA modeling environment allows the engineer to customize the modeling environment to match a design paradigm representative of the actual design. Previous modeling tools have a point-defined model space that forms the modeler to work in less than optimal environments. Current approaches for the problem to be bounded and constrained by requirements of the modeling tool and not the actual design problem. In some small cases, this is only maximally adequate MM facilitates the implementation of a surety methodology, which is used to represent high assurance systems with respect to safety and reliability. Formal mathematical models am used to correctly describe design safety and reliability functionality and behavioral fictional and behavioral representations of the design w then analyzed using commercial-off-the-shelf tools

LEESA: Embedding Strategic and XPath-Like Object Structure Traversals in C++

Abstract. Traversals of heterogeneous object structures are the most common operations in schema-first applications where the three key is-sues are (1) separation of traversal specifications from type-specific ac-tions, (2) expressiveness and reusability of traversal specifications, and (3) supporting structure-shy traversal specifications that require min-imal adaptation in the face of schema evolution. This paper presents Language for Embedded quEry and traverSAl (LEESA), which pro-vides a generative programming approach to address the above issues. LEESA is an object structure traversal language embedded in C++. Using C++ templates, LEESA combines the expressiveness of XPath’s axes-oriented traversal notation with the genericity and programmabil-ity of Strategic Programming. LEESA uses the object structure meta-information to statically optimize the traversals and check their compat-ibility against the schema. Moreover, a key usability issue of domain-specific error reporting in embedded DSL languages has been addressed in LEESA through a novel application of Concepts, which is an upcoming C++ standard (C++0x) feature. We present a quantitative evaluation of LEESA illustrating how it can significantly reduce the development efforts of schema-first applications.

Enabling Model Testing of Cyber-Physical Systems

Applying traditional testing techniques to Cyber-Physical Systems (CPS) is challenging due to the deep intertwining of software and hardware, and the complex, continuous interactions between the system and its environment. To alleviate these challenges we propose to conduct testing at early stages and over executable models of the system and its environment. Model testing of CPSs is however not without difficulties. The complexity and heterogeneity of CPSs renders necessary the combination of different modeling formalisms to build faithful models of their different components. The execution of CPS models thus requires an execution framework supporting the co-simulation of different types of models, including models of the software (e.g., SysML), hardware (e.g., SysML or Simulink), and physical environment (e.g., Simulink). Furthermore, to enable testing in realistic conditions, the co-simulation process must be (1) fast, so that thousands of simulations can be conducted in practical time, (2) controllable, to precisely emulate the expected runtime behavior of the system and, (3) observable, by producing simulation data enabling the detection of failures. To tackle these challenges, we propose a SysML-based modeling methodology for model testing of CPSs, and an efficient SysML-Simulink co-simulation framework. Our approach was validated on a case study from the satellite domain

Let’s Get Physical: Computer Science Meets Systems

In cyber-physical systems (CPS) computing, networking and control (typically regarded as the "cyber" part of the system) are tightly intertwined with mechanical, electrical, thermal, chemical or biological processes (the "physical" part). The increasing sophistication and heterogeneity of these systems requires radical changes in the way sense-and-control platforms are designed to regulate them. In this paper, we highlight some of the design challenges due to the complexity and heterogeneity of CPS. We argue that such challenges can be addressed by leveraging concepts that have been instrumental in fostering electronic design automation while dealing with complexity in VLSI system design. Based on these concepts, we introduce a design methodology whereby platform-based design is combined with assume-guarantee contracts to formalize the design process and enable realization of CPS architectures and control software in a hierarchical and compositional manner. We demonstrate our approach on a prototype design of an aircraft electric power system. © 2014 Springer-Verlag Berlin Heidelberg