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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Text-based information about past engineering failures presents one possible solution by facilitating the retrieval of information that can inform new designs. However, identifying documents containing relevant information and extracting required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing-based framework is proposed to discover relevant knowledge from documents containing failure-related design information. Documents containing usable information are filtered using sentiment analysis based on a custom lexicon specialized for engineering design and by filtering out documents containing only irrelevant topics. Next, from the identified usable documents, information relating to engineering failures, contributing factors that can be controlled at design time (“risk factors”), and recommended preventative actions are extracted. Semantic similarity is then used to group similar pieces of extracted information for improved generalizability. The proposed framework is applied to NASA’s Lessons Learned Information System (LLIS). The framework can be used to identify documents containing usable failure-related design information from other databases, extract relevant information from these documents, and generalize the acquired knowledge such that it can be applied to novel systems.

Sequoia R. Andrade↗
NASA NTRSDate not supplied

m:N ConOps/R&R Remote Simulation

This presentation details the experimental design of an investigation of small unmanned aircraft system (sUAS) operations involving multiple vehicle management by a remote operator. The study is part of an ongoing effort to explore multiple vehicle control by multiple operators, i.e., the control of N vehicles by m operators (m:N operations). For this effort, NASA and collaborators have developed prototypes of a concept of operations (ConOps), roles and responsibilities (R&R) for operators and supervisors, and a ground control station (GCS), including software displays and interfaces. Participants in this study acted as the pilot-in-command of twelve aircraft flying pre-approved routes in a simulation of a food delivery operation utilizing sUAS in the San Diego, CA area. Each participant experienced four experimental trials. Twice within the course of each trial, participants were responsible for responding to a sudden, unanticipated, and high-priority contingency: an airspace restriction for sUAS operations known as a UAS Volume Reservation (UVR). Upon issuance of a UVR, pilots were expected to reroute affected vehicles around the airspace. The level of automation (LoA) and workload of the flight rerouting task were varied. The LoA was manipulated by providing reroute suggestions ("auto" condition) for aircraft or by requiring pilots to manually reroute ("manual" condition) affected vehicles. Workload was varied as a function of the number of vehicles affected by the UVR contingencies: 2 vehicles ("low workload" condition) versus 4 vehicles ("high workload" condition). Additionally, some vehicles required pilots to adjust for terrain conflicts while avoiding the UVR region. Due to the COVID-19 pandemic, in-person data collection for this study was not possible. Researchers adapted to this circumstance through the development of remote data collection protocol. Participants were able to view adapted GCS displays using the Microsoft Teams teleconferencing platform and responded to events by using a verbal protocol developed for the experiment. Using this protocol, participants provided instructions for actions to a researcher, referred to as the surrogate, to carry out on their behalf. This presentation describes the experiment design, including special details for remote data collection via a subject-surrogate configuration, and concludes with planned data analysis and results to be presented at a later date.

multi-UAS↗
NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Information about past engineering failures in natural language format presents one possible solution by enabling the retrieval of information that can inform new designs. However, identifying documents containing usable information and extracting the required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing (NLP) framework is proposed to discover relevant knowledge from documents containing failure-related design information. The framework is applied to NASA’s Lessons Learned Information System (LLIS),which is publicly available. Documents containing usable information are filtered using two different NLP-based models. Next, from the identified usable documents, a failure taxonomy is extracted using a partitioned hierarchical topic modeling approach. Partitions of the document describe different sections of the failure taxonomy – i.e., failure, cause of failure, and recommendations – as indicated by the structure of the original document. The extracted failure taxonomy can be leveraged in early design failure assessment methods. Moreover, the framework can be used to identify documents containing usable failure-related design information from other databases and extract relevant information from these documents.

Documentation and Information Science↗
NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Information about past engineering failures in natural language format presents one possible solution by enabling the retrieval of information that can inform new designs. However, identifying documents containing usable information and extracting the required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing (NLP) framework is proposed to discover relevant knowledge from documents containing failure-related design information. The framework is applied to NASA’s Lessons Learned Information System (LLIS),which is publicly available. Documents containing usable information are filtered using two different NLP-based models. Next, from the identified usable documents, a failure taxonomy is extracted using a partitioned hierarchical topic modeling approach. Partitions of the document describe different sections of the failure taxonomy – i.e., failure, cause of failure, and recommendations – as indicated by the structure of the original document. The extracted failure taxonomy can be leveraged in early design failure assessment methods. Moreover, the framework can be used to identify documents containing usable failure-related design information from other databases and extract relevant information from these documents.

Documentation and Information Science↗
NASA NTRS2022

m:N Working Group Status Report

This document reports on the status of the m:N working group including the barriers, sub-group status, DAA white paper and roadmapping exercise.

multi-vehicle control↗
NASA NTRSDate not supplied

A New Control Paradigm: Multiple Aircraft Controlled by Multiple Operators

Remotely piloted aircraft systems (RPAS) are becoming more and more prevalent in the aerospace operations. This is true in a number of diverse domains; urban air mobility, medical product delivery, infrastructure inspection, high altitude pseudo-satellites, search and rescue, auto cargo and several other applications. One aspect that all of these share in common is the need for scalability to be viable and continue to grow. The Association of Uncrewed Vehicle Systems International (AUVSI) develops an annual economic report. They project that in the first three years of integration more than 70,000 jobs will be created in the US alone, with an economic impact of more than $13.6 billion. This benefit will grow through 2025 when we foresee more than 100,000 jobs created and economic impact of $82 billion. For many of these domains to reach these levels and have the scalability needed, they will require a remote pilot to control multiple aircraft (1:N) or the extension of that, multiple pilots controlling multiple aircraft (m:N). This is a new control paradigm that raises multiple issues in various areas. The issues include regulatory, technical, safety, community acceptance and Human Factors. Human factors issues include displays, pilot workload, pilot situation awareness just to name a few. This panel brings together researchers, developers and operators that have been working in the area of m:N. They will discuss the need, the issues and some potential solutions.

multi-vehicle control↗
NASA NTRSDate not supplied

m:N: m Operators Controlling N Vehicles

UAS are growing quickly and the promise of economic growth is large and real. However, for many domains to realize this potential, multi-vehicle control by a single operator (or m:N) is required. NASA has stood up an industry/gov't working group to identify issues and barriers. This work will be reviewed.

multi-vehicle control↗
NASA NTRSDate not supplied

Preparation for an Earth Independent Medical Operations Demonstration using the Tempus ALS™ Medical Device

NASA’s exploration-class missions have severe resource constraints, long return trip durations, significant communication delays and limited resupply opportunities. Validation on the International Space Station (ISS) of medical devices that fit within Earth-Independent Medical Operations (EIMO) systems is a necessary preparation step. Key features of an EIMO medical system include: 1) technologies that support the prevention, diagnosis, and treatment of spaceflight medical events; 2) components that meet mass, volume, power and crew time/training constraints; 3) consideration of the medical skill level of the astronaut caregiver; 4) collection, storage and analysis of medical data within a central data architecture; and 5) incorporation of appropriate guidance and support tools that allow crew autonomy. The Human Research Program’s (HRP) Exploration Medical Capability (ExMC) Element and the Exploration Medical Integrated Product Team (XM-IPT) are planning an ISS technology demonstration to determine the feasibility of including a multifunctional medical device into an EIMO medical system. Demonstration Preparations: The Tempus ALS (Remote Diagnostic Technologies, Ltd., Philips Corp., Farnborough, UK) is a commercial-off-the-shelf medical device, with United States Food and Drug Administration clearance and is Conformité Européene marked in Europe. Several thousand units have been sold and are successfully operating in pre-hospital and in remote settings, such as by European Space Agency (ESA) flight surgeons and some of NASA’s commercial partners during post-flight medical operations. The Tempus ALS provides vital sign measurements such as blood pressure, electrocardiograms, heart rate, end tidal CO2, respiration rate, pulse oximetry and temperature. The device has ultrasound imaging and video laryngoscopy capabilities and has automatic or manual defibrillation modes for treating cardiac arrhythmias. It includes procedural guidance capabilities to assist in the collection of the vital sign measurements and it has various data transmission and report generation features. NASA’s HRP ExMC and XM-IPT are partnering with the ESA to demonstrate the Tempus ALS on ISS, with ESA manifesting the Tempus ALS and its accessories. ESA will compare performance of periodic health status exams and medical contingency drills performed nominally and with the Tempus ALS. NASA’s EIMO demonstration will include the use of Tempus ALS to diagnose a complaint of abdominal pain under increasingly independent circumstances. The caregiver must assess the present illness, collect vital sign measurements, and perform an abdominal ultrasound, under one of three communication situations, including real-time, with a several second delay and with a delay on the order of minutes. Expected Outcomes: Information will be gained about the feasibility, benefits, and challenges of using a multifunctional, all-in-one, medical device for medical diagnosis instead of separate devices with singular functionalities. Information will also be collected about performance differences as communication delays increase and available ground support decreases. Gaining this understanding will allow for further development of exploration medical system capabilities which takes the EIMO construct into consideration.

Beth Lewandowski↗
NASA NTRSDate not supplied

Where is the Human in the Loop? Human Factors Analysis of Extended Visual Line of Sight Unmanned Aerial System Operations within a Remote Operations Environment

Many envisioned technological and conceptual innovations focus on allocating more functions to automation, relegating the human as an afterthought if not a nuisance. Yet, until complete autonomy is realized, the human will remain “in the loop”. The National Aeronautics and Space Administration is supporting research for the development and maturation of automated technologies and architectures for the future of advanced air mobility. Standing up a remote operations center used to control, manage, and monitor multiple highly automated vehicles is an important step towards realizing the advanced air mobility vision. At the National Aeronautics and Space Administration’s Langley Research Center, a remote operation center exists and has been tested using humans piloting simulated vehicles. In this paper, we explore the human element within live flight operations that rely on increasingly automated technologies. Four ground control station operators performed multiple live flight operations. We employed a naturalistic approach and relied on qualitative data such as interviews and discussions with subject matter experts to help facilitate discovery. The present work evaluates the functions that the human and the automation had during the live operations, lists psychological constructs that may have promoted or reduced task performance, and provides recommendations on design of the remote operations environment and training of future ground control station operators.

Advanced Air Mobility↗
NASA NTRSDate not supplied

Where is the Human in the Loop? Human Factors Analysis of Extended Visual Line of Sight Unmanned Aerial System Operations within a Remote Operations Environment

Many envisioned technological and conceptual innovations focus on allocating more functions to automation, relegating the human as an afterthought if not a nuisance. Yet, until complete autonomy is realized, the human will remain “in the loop”. The National Aeronautics and Space Administration is supporting research for the development and maturation of automated technologies and architectures for the future of advanced air mobility. Standing up a remote operations center used to control, manage, and monitor multiple highly automated vehicles is an important step towards realizing the advanced air mobility vision. At the National Aeronautics and Space Administration’s Langley Research Center, a remote operation center exists and has been tested using humans piloting simulated vehicles. In this paper, we explore the human element within live flight operations that rely on increasingly automated technologies. Four ground control station operators performed multiple live flight operations. We employed a naturalistic approach and relied on qualitative data such as interviews and discussions with subject matter experts to help facilitate discovery. The present work evaluates the functions that the human and the automation had during the live operations, lists psychological constructs that may have promoted or reduced task performance, and provides recommendations on design of the remote operations environment and training of future ground control station operators.

Advanced Air Mobility↗
NASA NTRS2019

Evaluation of Technology Concepts for Traffic Data Management and Relevant Audio for Datalink in Commercial Airline Flight Decks

Datalink is currently operational for departure clearances and in oceanic environments and is currently being tested in high altitude domestic enroute airspace. Interaction with even simple datalink clearances may create more workload for flight crews than the voice system they replace if not carefully designed. Datalink may also introduce additional complexity for flight crews with hundreds of uplink messages now defined for use. Finally, flight crews may lose airspace awareness and operationally relevant information that they normally pickup from Air Traffic Control (ATC) voice communications with other aircraft (i.e., “party-line” transmissions). Once again, automation may be poised to increase workload on the flight deck for incremental benefit. Datalink implementation to support future air traffic management concepts needs to be carefully considered, understanding human communication norms and especially, the change from voice- to text-based communications modality and its effect on pilot workload and situation awareness. Increasingly autonomous systems, where autonomy is designed to support human-autonomy teaming, may be suited to solve these issues. NASA is conducting research and development of increasingly autonomous systems, utilizing machine-learning algorithms seamlessly integrated with humans whereby task performance of the combined system is significantly greater than the individual components. Increasingly autonomous systems offer the potential for significantly improved levels of performance and safety that are superior to either human or automation alone. Two increasingly autonomous systems concepts - a traffic data manager and a conversational co-pilot - were developed to intelligently address the datalink issues in a complex, future state environment with significant levels of traffic. The system was tested for suitability of datalink usage for terminal airspace. The traffic data manager allowed for automated declutter of the Automatic Dependent Surveillance-Broadcast (ADS-B) display. The system determined relevant traffic for display based on machine learning algorithms trained by experienced human pilot behaviors. The conversational co-pilot provided relevant audio air traffic control messages based on context and proximity to ownship. Both systems made use of the connected aircraft concepts to provide intelligent context to determine relevancy above and beyond proximity to ownship. A human-in-the-loop test was conducted in NASA Langley Research Center’s Integration Flight Deck B-737-800 simulator to evaluate the traffic data manager and the conversational co-pilot. Twelve airline crews flew various normal and non-normal procedures and their actions and performance were recorded in response to the procedural events. This paper details the flight crew performance and evaluation during the events.

Etherington, Timothy↗
DOE OSTI2025

Human Factors Considerations for Anticipated Uses of Autonomy in Advanced Reactor Technologies

The objective of this report is examine anticipated advanced reactor designs that incorporate high levels of autonomy in operation of advanced reactor systems or facilities, and to begin identifying the human factors engineering (HFE) considerations relevant to those designs. The insights gained will help identify areas where U.S. Nuclear Regulatory Commission (NRC) may need to develop or update review guidance to adequately address HFE considerations essential for safe operation. The adequacy of existing guidance, along with recommendations for updates or new guidance, may be addressed in future reports. We use the term “landscape analysis” to describe the review, analysis, and characterization of publicly available information on the anticipated future state of the industry regarding particular technologies or operational concepts. This landscape analysis considers the potential range of advanced reactor automation concepts that may be used for monitoring and controlling nuclear systems and facilities, including the potential for highly automated, near autonomous, or fully autonomous operation of advanced nuclear reactors.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS2018

Increasing Human Spaceflight Capabilities: Demonstration of Crew Autonomy Through Self-Scheduling Onboard International Space Station

For the first time in a spaceflight operational environment, our team enabled an ISS (International Space Station) crewmember to plan, reschedule, and execute their activities in real-time while abiding by flight and scheduling constraints. The Crew Autonomous Scheduling Test (CAST) investigated the novel concept of operations: allowing crew to manage their own timeline.

crew autonomy↗