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Contents include the following: NASA Missions - implications to reliability and radiation constraints. Approach to Insertion of New Technologies Technology Knowledge-base development. Technology model/tool development and validation. Summary comments.
ISHM capability includes: detection of anomalies, diagnosis of causes of anomalies, prediction of future anomalies, and user interfaces that enable integrated awareness (past, present, and future) by users. This is achieved by focused management of data, information and knowledge (DIaK) that will likely be distributed across networks. Management of DIaK implies storage, sharing (timely availability), maintaining, evolving, and processing. Processing of DIaK encapsulates strategies, methodologies, algorithms, etc. focused on achieving high ISHM Functional Capability Level (FCL). High FCL means a high degree of success in detecting anomalies, diagnosing causes, predicting future anomalies, and enabling health integrated awareness by the user. A model that enables ISHM capability, and hence, DIaK management, is denominated the ISHM Model of the System (IMS). We describe aspects of the IMS that focus on processing of DIaK. Strategies, methodologies, and algorithms require proper context. We describe an approach to define and use contexts, implementation in an object-oriented software environment (G2), and validation using actual test data from a methane thruster test program at NASA SSC. Context is linked to existence of relationships among elements of a system. For example, the context to use a strategy to detect leak is to identify closed subsystems (e.g. bounded by closed valves and by tanks) that include pressure sensors, and check if the pressure is changing. We call these subsystems Pressurizable Subsystems. If pressure changes are detected, then all members of the closed subsystem become suspect of leakage. In this case, the context is defined by identifying a subsystem that is suitable for applying a strategy. Contexts are defined in many ways. Often, a context is defined by relationships of function (e.g. liquid flow, maintaining pressure, etc.), form (e.g. part of the same component, connected to other components, etc.), or space (e.g. physically close, touching the same common element, etc.). The context might be defined dynamically (if conditions for the context appear and disappear dynamically) or statically. Although this approach is akin to case-based reasoning, we are implementing it using a software environment that embodies tools to define and manage relationships (of any nature) among objects in a very intuitive manner. Context for higher level inferences (that use detected anomalies or events), primarily for diagnosis and prognosis, are related to causal relationships. This is useful to develop root-cause analysis trees showing an event linked to its possible causes and effects. The innovation pertaining to RCA trees encompasses use of previously defined subsystems as well as individual elements in the tree. This approach allows more powerful implementations of RCA capability in object-oriented environments. For example, if a pressurizable subsystem is leaking, its root-cause representation within an RCA tree will show that the cause is that all elements of that subsystem are suspect of leak. Such a tree would apply to all instances of leak-events detected and all elements in all pressurizable subsystems in the system. Example subsystems in our environment to build IMS include: Pressurizable Subsystem, Fluid-Fill Subsystem, Flow-Thru-Valve Subsystem, and Fluid Supply Subsystem. The software environment for IMS is designed to potentially allow definition of any relationship suitable to create a context to achieve ISHM capability.
The purpose of the APPL Masters Forum is to bring together some of the best project managers at NASA, as well as those in industry and other government agencies, for 2 1/2 days of knowledge sharing. The project managers come eager to reflect on their project experiences, to learn new things from one another--and to unlearn a few things, too. This was the fourth Masters Forum, and the first one held outside Washington, DC. Fifty participants from across the country came to Dallas at the American Airlines Conference Center, a wonderful facility that was conveniently located by the airport and yet still seemed isolated from the rest of the world. Masters Forum IV was also the first one held during the winter. Previous Masters Forums have been during the summer. Hot, sticky Washington, D.C. in the summer may sound unpleasant, but frankly the popularity of earlier Forums is what led to this annual event becoming a semiannual one.
In the development of complex software systems, designers are required to obtain from many sources and manage vast amounts of knowledge of the system being built and communicate this information to personnel with a variety of backgrounds. Knowledge concerning the properties of the system, including the structure of, relationships between and limitations of the data objects in the system, becomes increasingly more vital as the complexity of the system and the number of knowledge sources increases. Ensuring that violations of these properties do not occur becomes steadily more challenging. One approach toward managing the enforcement or system properties, called context monitoring, uses a centralized repository of integrity constraints and a constraint satisfiability mechanism for dynamic verification of property enforcement during program execution. The focus of this paper is to describe possible software architectures that define a mechanism for dynamically checking the satisfiability of a set of constraints on a program. The next section describes the context monitoring approach in general. Section 3 gives an overview of the work currently being done toward the addition of an integrity constraint satisfiability mechanism to a high-level program language, SequenceL, and demonstrates how this model is being examined to develop a general software architecture. Section 4 describes possible architectures for a general constraint satisfiability mechanism, as well as an alternative approach that, uses embedded database queries in lieu of an external monitor. The paper concludes with a brief summary outlining the, current state of the research and future work.
Aviation is a problem domain characterized by a high level of system complexity and uncertainty. Safety risk analysis in such a domain is especially challenging given the multitude of operations and diverse stakeholders. The Federal Aviation Administration (FAA) projects that by 2025 air traffic will increase by more than 50 percent with 1.1 billion passengers a year and more than 85,000 flights every 24 hours contributing to further delays and congestion in the sky (Circelli, 2011). This increased system complexity necessitates the application of structured safety risk analysis methods to understand and eliminate where possible, reduce, and/or mitigate risk factors. The use of expert judgments for probabilistic safety analysis in such a complex domain is necessary especially when evaluating the projected impact of future technologies, capabilities, and procedures for which current operational data may be scarce. Management of an R&D product portfolio in such a dynamic domain needs a systematic process to elicit these expert judgments, process modeling results, perform sensitivity analyses, and efficiently communicate the modeling results to decision makers. In this paper a case study focusing on the application of an R&D portfolio of aeronautical products intended to mitigate aircraft Loss of Control (LOC) accidents is presented. In particular, the knowledge elicitation process with three subject matter experts who contributed to the safety risk model is emphasized. The application and refinement of a verbal-numerical scale for conditional probability elicitation in a Bayesian Belief Network (BBN) is discussed. The preliminary findings from this initial step of a three-part elicitation are important to project management practitioners as they illustrate the vital contribution of systematic knowledge elicitation in complex domains.
Land cover modeling is used to inform land management, but most often via a two-step process where science informs how management alternatives can influence resources and then decision makers can use this to make decisions. A more efficient process is to directly integrate science and decision making, where science allows us to learn to better accomplish management objectives and is developed to address specific decisions. Co-development of management and science is especially productive when decisions are complicated by multiple objectives and impeded by uncertainty. Multiple objectives can be met by specification of tradeoffs, and relevant uncertainty can be addressed through targeted science (i.e., models and monitoring). We describe how to integrate habitat and fuels monitoring with decision making focused on dual objectives of managing for endangered species and minimizing catastrophic fire risk. Under certain conditions, both objectives might be achieved by a similar management policy, but habitat trajectories suggest tradeoffs. Knowledge about system responses to actions can be informed by applying competing management actions to different land units in the same system state and by ideas about fire behavior. Monitoring and management integration is important to optimize state-specific management decisions and increase knowledge about system responses. We believe this approach has broad utility for and cover modeling programs intended to inform decision making.
Intelligent Integrated System Health Management (ISHM) is the management of data, information, and knowledge (DIaK) with the purposeful objective of determining the health of a system (Management: storage, distribution, sharing, maintenance, processing, reasoning, and presentation). Presentation discusses: (1) ISHM Capability Development. (1a) ISHM Knowledge Model. (1b) Standards for ISHM Implementation. (1c) ISHM Domain Models (ISHM-DM's). (1d) Intelligent Sensors and Components. (2) ISHM in Systems Design, Engineering, and Integration. (3) Intelligent Control for ISHM-Enabled Systems
The seminar presents papers on modeling and simulation methodology, artificial intelligence and expert systems, environments for simulation/expert system development, and methodology for simulation/expert system development. Particular attention is given to simulation modeling concepts and their representation, modular hierarchical model specification, knowledge representation, and rule-based diagnostic expert system development. Other topics include the combination of symbolic and discrete event simulation, real time inferencing, and the management of large knowledge-based simulation projects.
The design of NASA's space station has begun. During the design cycle, and after activation of the space station, the reoccurring need will exist to access not only designs, but also deeper knowledge about the designs, which is only hinted in the design definition. Areas benefiting from this knowledge include training, fault management, and onboard automation. NASA's Artificial Intelligence Office at Johnson Space Center and The MITRE Corporation have conceptualized an approach for capture and storage of design knowledge.
The design of NASA's space station has begun. During the design cycle, and after activation of the space station, the reoccurring need will exist to access not only designs, but also deeper knowledge about the designs, which is only hinted in the design definition. Areas benefiting from this knowledge include training, fault management, and onboard automation. NASA's Artificial Intelligence Office at Johnson Space Center and The MITRE Corporation have conceptualized an approach for capture and storage of design knowledge.
The design of the two-phase flow systems which are anticipated to be utilized in future spacecraft thermal management systems requires a knowledge of two-phase flow and heat transfer phenomena in reduced gravities. This program was funded by NASA headquarters in response to NRA-91-OSSA-17 and was managed by Lewis Research Center. The main objective of this program was to design and construct a two-phase test loop, and perform a series of normal gravity and aircraft trajectory experiments to study the effect of gravity on the Critical Heat Flux (CHF) and onset of instability. The test loop was packaged on two aircraft racks and was also instrumented to generate data for two-phase pressure drop. The normal gravity tests were performed with vertical up and downflow configurations to bound the effect of gravity on the test parameters. One set of aircraft trajectory tests was performed aboard the NASA DC-9 aircraft. These tests were mainly intended to evaluate the test loop and its operational performance under actual reduced gravity conditions, and to produce preliminary data for the test parameters. The test results were used to demonstrate the applicability of the normal gravity models for prediction of the two-phase friction pressure drop. It was shown that the two-phase friction multipliers for vertical upflow and reduced gravity conditions can be successfully predicted by the appropriate normal gravity models. Limited critical heat flux data showed that the measured CHF under reduced gravities are of the same order of magnitude as the test results with vertical upflow configuration. A simplified correlation was only successful in predicting the measured CHF for low flow rates. Instability tests with vertical upflow showed that flow becomes unstable and critical heat flux occurs at smaller powers when a parallel flow path exists. However, downflow tests and a single reduced gravity instability experiment indicated that the system actually became more stable with a parallel single-phase flow path. Several design modifications have been identified which will improve the system performance for generating reduced gravity data. The modified test loop can provide two-phase flow data for a range of operating conditions and can serve as a test bed for component evaluation.
Abstract Process management combines complementary approaches of heuristic reasoning and analytical process control. Management of a continuous process requires monitoring the environment and the controlled system, assessing the ongoing situation, developing and revising planned actions, and controlling the execution of the actions. For knowledge-intensive domains, process management entails the potentially time-stressed cooperation among a variety of expert systems. By redesigning a blackboard control architecture in an object-oriented framework, researchers obtain an approach to process management that considerably extends blackboard control mechanisms and overcomes limitations of blackboard systems.
Computer vision, especially color image analysis and understanding, has much to offer in the area of the automation of Space Station tasks such as construction, satellite servicing, rendezvous and proximity operations, inspection, experiment monitoring, data management and training. Knowledge-based techniques improve the performance of vision algorithms for unstructured environments because of their ability to deal with imprecise a priori information or inaccurately estimated feature data and still produce useful results. Conventional techniques using statistical and purely model-based approaches lack flexibility in dealing with the variabilities anticipated in the unstructured viewing environment of space. Algorithms developed under NASA sponsorship for Space Station applications to demonstrate the value of a hypothesized architecture for a Video Image Processor (VIP) are presented. Approaches to the enhancement of the performance of these algorithms with knowledge-based techniques and the potential for deployment of highly-parallel multi-processor systems for these algorithms are discussed.
Integrating the respective functionality and architectural features of knowledge base and data base management systems is a topic of considerable interest. Several aspects of this topic and associated issues are addressed. The significance of integration and the problems associated with accomplishing that integration are discussed. The shortcomings of current approaches to integration and the need to fuse the capabilities of both knowledge base and data base management systems motivates the investigation of information processing paradigms. One such paradigm is concept based processing, i.e., processing based on concepts and conceptual relations. An approach to robust knowledge and data base system integration is discussed by addressing progress made in the development of an experimental model for conceptual information processing.
The Technical and Management Information System (TMIS) must employ on enlightened approach to its object structure, but basic issues in conceptual structuring remain to be resolved. Sirius outlines the necessary agenda and reports on progress toward solutions.
No abstract available
The implementation of an integrated system health management (ISHM) capability is fundamentally linked to the management of data, information, and knowledge (DIaK) with the purposeful objective of determining the health of a system. Management implies storage, distribution, sharing, maintenance, processing, reasoning, and presentation. ISHM is akin to having a team of experts who are all individually and collectively observing and analyzing a complex system, and communicating effectively with each other in order to arrive at an accurate and reliable assessment of its health. In this chapter, concepts, procedures, and approaches are presented as a foundation for implementing an ISHM capability relevant to intelligent systems. The capability stresses integration of DIaK from all elements of a system, emphasizing an advance toward an on-board, autonomous capability. Both ground-based and on-board ISHM capabilities are addressed. The information presented is the result of many years of research, development, and maturation of technologies, and of prototype implementations in operational systems.
The ability to learn from both the mistakes and successes of the past is vital to assuring success in the future. Due to the close physical interaction between spacesuit systems and human beings as users, spacesuit technology and usage lends itself rather uniquely to the benefits realized from the skillful organization of historical information; its dissemination; the collection and identification of artifacts; and the education of those in the field. The National Aeronautics and Space Administration (NASA), other organizations and individuals have been performing United States (U.S.) Spacesuit Knowledge Capture since the beginning of space exploration. Avenues used to capture the knowledge have included publication of reports; conference presentations; specialized seminars; and classes usually given by veterans in the field. More recently the effort has been more concentrated and formalized whereby a new avenue of spacesuit knowledge capture has been added to the archives in which videotaping occurs engaging both current and retired specialists in the field presenting technical scope specifically for education and preservation of knowledge. With video archiving, all these avenues of learning can now be brought to life with the real experts presenting their wealth of knowledge on screen for future learners to enjoy. Scope and topics of U.S. spacesuit knowledge capture have included lessons learned in spacesuit technology, experience from the Gemini, Apollo, Skylab and Shuttle programs, hardware certification, design, development and other program components, spacesuit evolution and experience, failure analysis and resolution, and aspects of program management. Concurrently, U.S. spacesuit knowledge capture activities have progressed to a level where NASA, the National Air and Space Museum (NASM), Hamilton Sundstrand (HS) and the spacesuit community are now working together to provide a comprehensive closed-looped spacesuit knowledge capture system which includes