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NASA NTRS2010

AIAA Aerospace America Magazine - Year in Review Article, 2010

NASA Stennis Space Center has implemented a pilot operational Integrated System Health Management (ISHM) capability. The implementation was done for the E-2 Rocket Engine Test Stand and a Chemical Steam Generator (CSG) test article; and validated during operational testing. The CSG test program is a risk mitigation activity to support building of the new A-3 Test Stand, which will be a highly complex facility for testing of engines in high altitude conditions. The foundation of the ISHM capability are knowledge-based integrated domain models for the test stand and CSG, with physical and model-based elements represented by objects the domain models enable modular and evolutionary ISHM functionality.

Figueroa, Fernando↗

NASA NTRS1996

Integrating a Genetic Algorithm Into a Knowledge-Based System for Ordering Complex Design Processes

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to be able to determine the best ordering of the processes within these subcycles to reduce design cycle time and cost. Many decomposition approaches assume the capability is available to determine what design processes and couplings exist and what order of execution will be imposed during the design cycle. Unfortunately, this is often a complex problem and beyond the capabilities of a human design manager. A new feature, a genetic algorithm, has been added to DeMAID (Design Manager's Aid for Intelligent Decomposition) to allow the design manager to rapidly examine many different combinations of ordering processes in an iterative subcycle and to optimize the ordering based on cost, time, and iteration requirements. Two sample test cases are presented to show the effects of optimizing the ordering with a genetic algorithm.

Rogers, James L.↗

NASA NTRS2007

Integration and Testing Challenges of Small Satellite Missions: Experiences from the Space Technology 5 Project

The Space Technology 5(ST5) payload was successfully carried into orbit on an OSC Pegasus XL launch vehicle, which was carried aloft and dropped from the OSC Lockheed L-1011 from Vandenberg Air Force Base March 22,2006, at 9:03 am Eastern time, 6:03 am Pacific time. In order to reach the completion of the development and successful launch of ST 5, the systems integration and test(I&T) team determined that a different approach was required to meet the project requirements rather than the standard I&T approach used for single, room-sized satellites. The ST5 payload, part of NASA's New Millennium Program headquartered at JPL, consisted of three micro satellites (approximately 30 kg each) and the Pegasus Support Structure (PSS), the system that connected the spacecrafts to the launch vehicle and deployed the spacecrafts into orbit from the Pegasus XL launch vehicle. ST5 was a technology demonstration payload, intended to test six (6) new technologies for potential use for future space flights along with demonstrating the ability of small satellites to perform quality science. The main technology was a science grade magnetometer designed to take measurements of the earth's magnetic field. The three spacecraft were designed, integrated, and tested at NASA Goddard Space Flight Center with integration and environmental testing occurring in the Bldg. 7-1 0-15-29. The three spacecraft were integrated and tested by the same I&T team. The I&T Manager determined that there was insufficient time in the schedule to perform the three I&T spacecraft activities in series used standard approaches. The solution was for spacecraft #1 to undergo integration and test first, followed by spacecraft #2 and #3 simultaneously. This simultaneous integration was successful for several reasons. Each spacecraft had a Lead Test Conductor who planned and coordinated their spacecraft through its integration and test activities. One team of engineers and technicians executed the integration of all three spacecraft, learning and gaining knowledge and efficiency as spacecraft #1 integration and testing progressed. They became acutely familiar with the hardware, operation and processes for I&T, thus each team member had the experience and knowledge to safely execute I&T for spacecraft #2 and #3 together. The integration team was very versatile and each member could perform many different activities or work any spacecraft, when needed. Daily meetings between the three Lead TCs and technician team allowed the team to plan and implement activities efficiently. The three (3) spacecraft and PSS were successfully integrated and tested, shipped to the launch site, and ready for launch per the I&T schedule that was planned three years previously.

Sauerwein, Timothy A.↗

NASA NTRS2008

Integration and Testing Challenges of Small, Multiple Satellite Missions: Experiences from the Space Technology 5 Project

The ST5 technology demonstration mission led by GSFC of NASA's New Millennium Program managed by JPL consisted of three micro satellites (approximately 30 kg each) deployed into orbit from the Pegasus XL launch vehicle. In order to meet the launch date schedule of ST5, a different approach was required rather than the standard I&T approach used for single, room-sized satellites. The three spacecraft were designed, integrated, and tested at NASA Goddard Space Flight Center. It was determined that there was insufficient time in the schedule to perform three spacecraft I&T activities in series using standard approaches. The solution was for spacecraft #1 to undergo integration and test first, followed by spacecraft #2 and #3 simultaneously. This simultaneous integration was successful for several reasons. Each spacecraft had a Lead Test Conductor who planned and coordinated their spacecraft through its integration and test activities. One team of engineers and technicians executed the integration of all three spacecraft, learning and gaining knowledge and efficiency as spacecraft #1 integration and testing progressed. They became acutely familiar with the hardware, operation and processes for I&T, thus had the experience and knowledge to safely execute I&T for spacecraft #2 and #3. The integration team was extremely versatile; each member could perform many different activities or work any spacecraft, when needed. ST5 was successfully integrated, tested and shipped to the launch site per the I&T schedule that was planned three years previously. The I&T campaign was completed with ST5's successful launch on March 22, 2006.

Sauerwein, Timothy A.↗

NASA NTRS2010

Developing Systems Engineering Skills Through NASA Summer Intern Project

During the Formulation phases of the NASA Project Life Cycle, communication systems engineers are responsible for designing space communication links and analyzing their performance to ensure that the proposed communication architecture is capable of satisfying high-level mission requirements. Senior engineers with extensive experience in communications systems perform these activities. However, the increasing complexity of space systems coupled with the current shortage of communications systems engineers has led to an urgent need for expedited training of new systems engineers. A pilot program, in which college-bound high school and undergraduate students studying various engineering disciplines are immersed in NASA s systems engineering practices, was conceived out of this need. This rapid summerlong training approach is feasible because of the availability of advanced software and technology tools and the students inherent ability to operate such tools. During this pilot internship program, a team of college-level and recently-hired engineers configured and utilized various software applications in the design and analysis of communication links for a plausible lunar sortie mission. The approach taken was to first design the direct-to-Earth communication links for the lunar mission elements, then to design the links between lunar surface and lunar orbital elements. Based on the data obtained from these software applications, an integrated communication system design was realized and the students gained valuable systems engineering knowledge. This paper describes this approach to rapidly training college-bound high school and undergraduate engineering students from various disciplines in NASA s systems engineering practices and tools. A summary of the potential use of NASA s emerging systems engineering internship program in broader applications is also described.

Bhasin, Kul↗

NASA NTRS2011

Autonomous, In-Flight Crew Health Risk Management for Exploration-Class Missions: Leveraging the Integrated Medical Model for the Exploration Medical System Demonstration Project

The Integrated Medical Model (IMM) captures organizational knowledge across the space medicine, training, operations, engineering, and research domains. IMM uses this knowledge in the context of a mission and crew profile to forecast risks to crew health and mission success. The IMM establishes a quantified, statistical relationship among medical conditions, risk factors, available medical resources, and crew health and mission outcomes. These relationships may provide an appropriate foundation for developing an in-flight medical decision support tool that helps optimize the use of medical resources and assists in overall crew health management by an autonomous crew with extremely limited interactions with ground support personnel and no chance of resupply.

Butler, D. J.↗

DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗

DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRSDate not supplied

Airspace Research and Development Portfolio Assessment of Urban Air Mobility using Knowledge Graph Data Science

National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of Urban Air Mobility (UAM) operations. The UAM vision is one in which advanced technologies and new operational procedures enable practical and cost-effective air transport as an integrated mode of movement of people and goods throughout metropolitan areas. To safely support UAM operations at scale in the National Airspace System (NAS), NASA’s Air Traffic Management-Exploration (ATM-X) project has been conducting research that evolves the UAM air traffic management system towards a highly automated and operationally flexible system of the future. The complexity of UAM airspace evolution to accommodate the increasing tempo of UAM operations over time is managed through the UAM airspace research roadmap, which is a system engineering approach to the R&D of complex system-of-systems, where system’s interdependencies make it nearly impossible to define requirements for individual elements of the system in isolation. These interdependencies form a knowledge graph (node-link network) with a highly complex structure far beyond the human user’s ability to extract insights for project management’s research portfolio assessment. This study applies advanced data analytics in knowledge graph to the UAM knowledge graph to facilitate the portfolio assessment.

ATM↗

NASA NTRSDate not supplied

Airspace Research and Development Portfolio Assessment of Urban Air Mobility using Knowledge Graph Data Science

ATM↗

NASA NTRS1992

There is No Free Lunch: Tradeoffs in the Utility of Learned Knowledge

With the recent introduction of learning in integrated systems, there is a need to measure the utility of learned knowledge for these more complex systems. A difficulty arrises when there are multiple, possibly conflicting, utility metrics to be measured. In this paper, we present schemes which trade off conflicting utility metrics in order to achieve some global performance objectives. In particular, we present a case study of a multi-strategy machine learning system, mutual theory refinement, which refines world models for an integrated reactive system, the Entropy Reduction Engine. We provide experimental results on the utility of learned knowledge in two conflicting metrics - improved accuracy and degraded efficiency. We then demonstrate two ways to trade off these metrics. In each, some learned knowledge is either approximated or dynamically 'forgotten' so as to improve efficiency while degrading accuracy only slightly.

Kedar, Smadar T.↗

NASA NTRS1987

The IDEAL (Integrated Design and Engineering Analysis Languages) modeling methodology: Capabilities and Applications

The IDEAL (Integrated Design and Engineering Analysis Languages) modeling methodology has been formulated and applied over a five-year period. It has proven to be a unique, integrated approach utilizing a top-down, structured technique to define and document the system of interest; a knowledge engineering technique to collect and organize system descriptive information; a rapid prototyping technique to perform preliminary system performance analysis; and a sophisticated simulation technique to perform in-depth system performance analysis.

Evers, Ken H.↗

NASA NTRS2008

Development of a NASA Integrated Technical Workforce Career Development Model Entitled Requisite Occupation Competencies and Knowledge -- the ROCK

This paper shares the findings of NASA's Integrated Learning and Development Program (ILDP) in its effort to reinvigorate the HANDS-ON practice of space systems engineering and project/program management through focused coursework, training opportunities, on-the job learning and special assignments. Prior to March 2005, NASA responsibility for technical workforce development (the program/project manager, systems engineering, discipline engineering, discipline engineering and associated communities) was executed by two parallel organizations. In March 2005 these organizations merged. The resulting program-ILDP-was chartered to implement an integrated competency-based development model capable of enhancing NASA's technical workforce performance as they face the complex challenges of Earth science, space science, aeronautics and human spaceflight missions. Results developed in collaboration with NASA Field Centers are reported on. This work led to definition of the agency's first integrated technical workforce development model known as the Requisite Occupation Competence and Knowledge (the ROCK). Critical processes and products are presented including: 'validation' techniques to guide model development, the Design-A-CUrriculuM (DACUM) process, and creation of the agency's first systems engineering body-of-knowledge. Findings were validated via nine focus groups from industry and government, validated with over 17 space-related organizations, at an estimated cost exceeding $300,000 (US). Masters-level programs and training programs have evolved to address the needs of these practitioner communities based upon these results. The ROCK reintroduced rigor and depth to the practitioner's development in these critical disciplines enabling their ability to take mission concepts from imagination to reality.

Menrad, Robert J.↗

NASA NTRS1987

TDAS: The Thermal Expert System (TEXSYS) data acquisition system

As part of the NASA Systems Autonomy Demonstration Project, a thermal expert system (TEXSYS) is being developed. TEXSYS combines a fast real time control system, a sophisticated human interface for the user and several distinct artificial intelligence techniques in one system. TEXSYS is to provide real time control, operations advice and fault detection, isolation and recovery capabilities for the space station Thermal Test Bed (TTB). TEXSYS will be integrated with the TTB and act as an intelligent assistant to thermal engineers conducting TTB tests and experiments. The results are presented from connecting the real time controller to the knowledge based system thereby creating an integrated system. Special attention will be paid to the problem of filtering and interpreting the raw, real time data and placing the important values into the knowledge base of the expert system.

Hack, Edmund C.↗

NASA NTRS1990

The environmental control and life support system advanced automation project. Phase 1: Application evaluation

The Environmental Control and Life Support System (ECLSS) is a Freedom Station distributed system with inherent applicability to advanced automation primarily due to the comparatively large reaction times of its subsystem processes. This allows longer contemplation times in which to form a more intelligent control strategy and to detect or prevent faults. The objective of the ECLSS Advanced Automation Project is to reduce the flight and ground manpower needed to support the initial and evolutionary ECLS system. The approach is to search out and make apparent those processes in the baseline system which are in need of more automatic control and fault detection strategies, to influence the ECLSS design by suggesting software hooks and hardware scars which will allow easy adaptation to advanced algorithms, and to develop complex software prototypes which fit into the ECLSS software architecture and will be shown in an ECLSS hardware testbed to increase the autonomy of the system. Covered here are the preliminary investigation and evaluation process, aimed at searching the ECLSS for candidate functions for automation and providing a software hooks and hardware scars analysis. This analysis shows changes needed in the baselined system for easy accommodation of knowledge-based or other complex implementations which, when integrated in flight or ground sustaining engineering architectures, will produce a more autonomous and fault tolerant Environmental Control and Life Support System.

Dewberry, Brandon S.↗

NASA NTRS2019

Orbital Debris Ontology, Terminology, and Knowledge Modeling

The looming threat orbital debris poses to assets in orbit demands solutions. As the orbital population grows, so does this hazard, but so does the sea of data. The problem is also an opportunity for interdisciplinary innovation and cooperation. This paper focuses on the data and information management aspect of developing solutions for a sustainable and safe orbital space environment. The corresponding author’s in-progress work to develop an orbital debris domain ontology is summarized in order to discuss knowledge modeling for this domain. Methodological approaches of this effort can also contribute to standards efforts and address terminological and policy questions. Leveraging the growing volumes of orbital debris and space situational awareness (SSA) data will create a more complete picture of the orbital space environment. Part of the solution will be: consistent and correct data interpretation, sharing orbital debris and SSA data in one form or another, terminology development & harmonization, and knowledge or domain modeling. To facilitate this, [Rovetto, 2015/16] discussed ontology development for the orbital debris domain. This paper lists concepts from that paper, and subsequently developed concepts [2-9]. Ontology engineering is an interdisciplinary field related to knowledge representation and reasoning in artificial intelligence, semantic technologies and the so-called semantic web. An ontology is effectively a computable and semantically rich terminology that presents a knowledge or domain model for a topic area. Expressions of knowledge or assertions are stored using formally defined term. This knowledge base is reasoned over to yield answers to queries, among other things. Ontologies have been developed in knowledge-based projects across various disciplines, and used for such things as search engines, chatbots, enterprise knowledge graphs, etc. Ontologies support: interoperability, automated reasoning, data sharing and integration, data search and retrieval, and communicating the meaning of data. The Orbital Debris Ontology (ODO), and related ontologies [Rovetto & Kelso 2016] [Rovetto 2016, 2017], were proposed to help achieve this. ODO, for instance, is intended as a domain ontology that can be used across federated databases, offering an explicitly specified set of concepts describing the orbital debris domain. Its meaning-rich taxonomy will provide a sharable semantics for orbital debris data to, in part, consistently communicate the meaning of data to both humans and machines, and tag data elements in space object catalogs to help afford inference tasks, decision support, knowledge discovery, and information integration. ODO and the SSA ontology (SSAO) is part of the overall Orbital Space Domain Ontology concept, which is conceived as a broader domain reference ontology. It aims to provide a knowledge representation structure of the orbital space environment, a common semantic model, and develop a sharable terminology. Collectively this will provide common meaning for datasets, a high-level taxonomy or classification for orbital space objects, and thus means to characterize space objects. Ongoing efforts have included using visualizations, R, JSON-LD, and contemporary semantic technologies. Potential applications and interdisciplinary partnerships include web-based platforms, web apps, visualizations, and academia projects. Community input and participation may yield a more widely understood domain model as well as facilitate terminological standards. For example, the proposed conceptual, terminological and ontological analysis may contribute to such efforts as the Space Debris Mitigation Requirements in the International Standards Organization by developing more precise, consistent and coherent terms and definitions. Projects that seek to develop in-house ontologies can use ODO and related ontologies as domain reference ontologies. This paper was developed independent of author affiliations. Readers are encouraged to contact corresponding author(1) with general interest and potential opportunities to support or realize the described project.

Robert J. Rovetto↗

NASA NTRSDate not supplied

Leveraging Nurse Informaticists for Development of the Impact Tool

The American Nurses Association (ANA) defines nursing informatics as the integration of “nursing science, computer science, and information science to manage and communicate data, information, knowledge, and wisdom in nursing practice. [1]” In additional to nursing education and clinical experience, this field requires additional education in areas of data analysis, database management, and clinical information systems. Nursing informaticists help bridge the communication gap between clinical and technical stakeholders which help align common goals. NASA is currently developing a new medical system trade analysis and decision support tool called IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces). The development team is comprised of multiple specialties including but not limited to clinicians (pharmacists, physicians, and nurse informaticists), software and program developers, human factors experts, and engineers. The nurse informaticists play a significant role in the development, operation, and results interpretation of IMPACT. Their expert knowledge and skills in both clinical nursing, information science, and database management place them in a unique position to collaborate and integrate with the clinical, engineering, and software development members of the team to achieve a cohesive, functional, and user-friendly model.

Lynn Boley↗

NASA NTRS1987

Space shuttle main engine anomaly data and inductive knowledge based systems: Automated corporate expertise

Progress is reported on the development of SCOTTY, an expert knowledge-based system to automate the analysis procedure following test firings of the Space Shuttle Main Engine (SSME). The integration of a large-scale relational data base system, a computer graphics interface for experts and end-user engineers, potential extension of the system to flight engines, application of the system for training of newly-hired engineers, technology transfer to other engines, and the essential qualities of good software engineering practices for building expert knowledge-based systems are among the topics discussed.

Modesitt, Kenneth L.↗