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.

A References Architecture for Human Cyber Physical Systems, Part II: Fundamental Design Principles for Human-CPS Interaction

As automation increases qualitatively and quantitatively in safety-critical human cyber-physical systems, it is becoming more and more challenging to increase the probability or ensure that human operators still perceive key artifacts and comprehend their roles in the system. In the companion paper, we proposed an abstract reference architecture capable of expressing all classes of system-level interactions in human cyber-physical systems. Here we demonstrate how this reference architecture supports the analysis of levels of communication between agents and helps to identify the potential for misunderstandings and misconceptions. We then develop a metamodel for safe human machine interaction. Therefore, we ask what type of information exchange must be supported on what level so that humans and systems can cooperate as a team, what is the criticality of exchanged information, what are timing requirements for such interactions, and how can we communicate highly critical information in a limited time frame in spite of the many sources of a distorted perception. We highlight shared stumbling blocks and illustrate shared design principles, which rest on established ontologies specific to particular application classes. In order to overcome the partial opacity of internal states of agents, we anticipate a key role of virtual twins of both human and technical cooperation partners for designing a suitable communication

A Reference Architecture of Human Cyber-Physical Systems – Part I: Fundamental Concepts

We propose a reference architecture of safety-critical or industry-critical human cyber-physical systems (CPSs) capable of expressing essential classes of system-level interactions between CPS and humans relevant for the societal acceptance of such systems. To reach this quality gate, the expressivity of the model must go beyond classical viewpoints such as operational, functional, and architectural views and views used for safety and security analysis. The model does so by incorporating elements of such systems for mutual introspections in situational awareness, capabilities, and intentions to enable a synergetic, trusted relation in the interaction of humans and CPSs, which we see as a prerequisite for their societal acceptance. The reference architecture is represented as a metamodel incorporating conceptual and behavioral semantic aspects. We illustrate the key concepts of the metamodel with examples from cooperative autonomous driving, the operating room of the future, cockpit-tower interaction, and crisis management