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It will be the responsibility of the long-duration space flight crew to take the actions necessary to maintain their health and well-being and to cope with medical emergencies without direct assistance from support personnel, including maintaining mental health and managing physiological and psychological changes that may impair decision making and performance. The Behavior and Performance Integrated Product Team at Johnson Space Center, working, within the Space Medicine, Monitoring, and Countermeasures Program, has identified critical questions pertaining to long-duration space crew behavioral health, psychological adaptation, human factors and habitability, and sleep and circadian rhythms. Among the projects addressing these questions are: the development of tools to assess cognitive functions during space missions; the development of a model of psychological adaptation in isolated and confined environments; tools and methods for selecting individuals and teams well-suited for long-duration missions; identification of mission-critical tasks and performance evaluation; and measures of sleep quality and correlation to mission performance.
Low cost access to space is a primary goal for both NASA and the U.S. aerospace industry. Integrated subsystem health diagnostics is an area where major improvements have been identified for potential implementation into the design of new reusable launch vehicles (RLVS) in order to reduce life cycle costs, increase safety margins and improve mission reliability. A number of efforts are underway to use existing and emerging technologies to establish new methods for vehicle health monitoring on operational vehicles as well as X-vehicles. This paper summarizes a joint effort between several NASA centers and industry partners to develop rapid wireless diagnostic tools for failure management and long-term TPS performance monitoring of thermal protection systems (TPS) on future RLVS. An embedded wireless microsensor suite is being designed to allow rapid subsurface TPS health monitoring and damage assessment. This sensor suite will consist of both passive overlimit sensors and sensors for continuous parameter monitoring in flight. The on-board diagnostic system can be used to radio in maintenance requirements before landing and the data could also be used to assist in design validation for X-vehicles. For a 3rd generation vehicle, wireless diagnostics should be at a stage of technical development that will allow use for intelligent feedback systems for guidance and navigation control applications and can also serve as feedback for TPS that can intelligently adapt to its environment.
The United States Army Materiel Command, Army Acquisition Pollution Prevention Support Office (AAPPSO) is responsible for creating and managing the U.S. Army Wide Acquisition Pollution Prevention Program. They have established Integrated Process Teams (IPTs) within each of the Major Subordinate Commands of the Army Materiel Command. AAPPSO provides centralized integration, coordination, and oversight of the Army Acquisition Pollution Prevention Program (AAPPP) , and the IPTs provide the decentralized execution of the AAPPSO program. AAPPSO issues policy and guidance, provides resources and prioritizes P2 efforts. It is the policy of the (AAPPP) to require United States Army Surgeon General approval of all materials or substances that will be used as an alternative to existing hazardous materials, toxic materials and substances, and ozone-depleting substances. The Army has a formal process established to address this effort. Army Regulation 40-10 requires a Health Hazard Assessment (HHA) during the Acquisition milestones of a new Army system. Army Regulation 40-5 addresses the Toxicity Clearance (TC) process to evaluate new chemicals and materials prior to acceptance as an alternative. U.S. Army Center for Health Promotion and Preventive Medicine is the Army's matrixed medical health organization that performs the HHA and TC mission.
A major new NASA initiative on environmental change and health has been established to promote the application of Earth science remote sensing data, information, observations, and technologies to issues of human health. NASA's Earth Sciences suite of Earth observing instruments are now providing improved observations science, data, and advanced technologies about the Earth's land, atmosphere, and oceans. These new space-based resources are being combined with other agency and university resources, data integration and fusion technologies, geographic information systems (GIS), and the spectrum of tools available from the public health community, making it possible to better understand how the environment and climate are linked to specific diseases, to improve outbreak prediction, and to minimize disease risk. This presentation is an overview of NASA's tools, capabilities, and research advances in this initiative.
During the two-year project period, we have worked on several aspects of Health Usage and Monitoring Systems for structural health monitoring. In particular, we have made contributions in the following areas. 1. Reference HUMS architecture: We developed a high-level architecture for health monitoring and usage systems (HUMS). The proposed reference architecture is shown. It is compatible with the Generic Open Architecture (GOA) proposed as a standard for avionics systems. 2. HUMS kernel: One of the critical layers of HUMS reference architecture is the HUMS kernel. We developed a detailed design of a kernel to implement the high level architecture.3. Prototype implementation of HUMS kernel: We have implemented a preliminary version of the HUMS kernel on a Unix platform.We have implemented both a centralized system version and a distributed version. 4. SCRAMNet and HUMS: SCRAMNet (Shared Common Random Access Memory Network) is a system that is found to be suitable to implement HUMS. For this reason, we have conducted a simulation study to determine its stability in handling the input data rates in HUMS. 5. Architectural specification.
The International Space University (ISU) 2002 Summer Session was conducted in Pomona, California, June 29-August 30, 2002. Ninety-nine professionals and students from thirty-one countries attended the Summer Session. More than half of these students participated in the Student Research Design Project entitled, "HI-STAR: Health Improvements through Space Technologies and Resources." ISU's interdisciplinary Student Research Design Projects are intended to have great educational value for the participants and, at the same time, to result in a product that will be useful to the field. The HI-STAR project was a success on both counts. The mission of the ISU students' effort on HI-STAR was to develop and promote a global strategy to help combat malaria using space technology. Like the tiny yet powerful mosquito, HI-STAR is a small program that aspires to make a difference. Timely detection of malaria danger zones is essential to help health authorities and policy makers make decisions about how to manage limited resources for combating malaria. In 2001, the technical support network for prevention and control of malaria epidemics published a study called "Malaria Early Warning Systems: Concepts, Indicators and Partners." This study, funded by Roll Back Malaria, a World Health Organization initiative, offered a framework for a monitoring and early warning system. HI-STAR seeks to build on this proposal and enhance the space elements of the suggested framework. Malaria disease dynamics and distributions are related to environmental variables. From space, environmental conditions that support the growth of mosquito populations can be monitored, Malaria-specific information can be gathered from satellite-borne remote sensing instruments and ground-based sensors. This information can be integrated via geographic information systems (GIS) into a Malaria Information System (MIS) that can provide assessment analyses and risk maps as output. HI-STAR defines and suggests the development of an active MIS as a low-cost tool to help organizations plan their efforts to fight malaria.
NASA is focusing considerable efforts on technology development for Integrated Vehicle Health Management systems. The research in this area is targeted toward increasing aerospace vehicle safety and reliability, while reducing vehicle operating and maintenance costs. Onboard, real-time sensing technologies that can provide detailed information on structural integrity are central to such a health management system. This paper describes a number of sensor technologies currently under development for integrated vehicle health management. The capabilities, current limitations, and future research needs of these technologies are addressed.
Integrated Vehicle Health Management (IVHM) systems for aerospace vehicles, is the process of assessing, preserving, and restoring system functionality across flight and techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of Integrated Intelligent Vehicle Management (IIVM). These real-time responses allow the IIVM to modify the affected vehicle subsystem(s) prior to a catastrophic event. Furthermore, this framework integrates technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear that IIVM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission objectives. These systems include the following: Guidance and Navigation; Communications and Tracking; Vehicle Monitoring; Information Transport and Integration; Vehicle Diagnostics; Vehicle Prognostics; Vehicle Mission Planning, Automated Repair and Replacement; Vehicle Control; Human Computer Interface; and Onboard Verification and Validation. Furthermore, the presented framework provides complete vehicle management which not only allows for increased crew safety and mission success through new intelligence capabilities, but also yields a mechanism for more efficient vehicle operations.
Methods for structural health monitoring are now being assessed, especially in high-performance, extreme environment, safety-critical applications. One such application is for composite cryogenic fuel tanks. The work presented here attempts to characterize and investigate the feasibility of using imbedded piezoelectric sensors to detect cracks and delaminations under cryogenic and ambient conditions. Different types of excitation and response signals and different sensors are employed in composite plate samples to aid in determining an optimal algorithm, sensor placement strategy, and type of imbedded sensor to use. Variations of frequency and high frequency chirps of the sensors are employed and compared. Statistical and analytic techniques are then used to determine which method is most desirable for a specific type of damage and operating environment. These results are furthermore compared with previous work using externally mounted sensors. More work is needed to accurately account for changes in temperature seen in these environments and be statistically significant. Sensor development and placement strategy are other areas of further work to make structural health monitoring more robust. Results from this and other work might then be incorporated into a larger composite structure to validate and assess its structural health. This could prove to be important in the development and qualification of any 2nd generation reusable launch vehicle using composites as a structural element.
Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.
The Human Health and Support Systems Capability Roadmap focuses on research and technology development and demonstration required to ensure the health, habitation, safety, and effectiveness of crews in and beyond low Earth orbit. It contains three distinct sub-capabilities: Human Health and Performance. Life Support and Habitats. Extra-Vehicular Activity.
As a part of the overall goal of developing Integrated Vehicle Health Management systems for aerospace vehicles, the NASA Faculty Fellowship Program (NFFP) at Marshall Space Flight Center has performed a pilot study on IVHM principals which integrates researched IVHM technologies in support of Integrated Intelligent Vehicle Management (IIVM). IVHM is the process of assessing, preserving, and restoring system functionality across flight and ground systems (NASA NGLT 2004). The framework presented in this paper integrates advanced computational techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of INM. These real-time responses allow the IIVM to modify the affected vehicle subsystem(s) prior to a catastrophic event. Furthermore, the objective of this pilot program is to develop and integrate technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear the INM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition, to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission objectives: Guidance and Navigation; Communications and Tracking; Vehicle Monitoring; Information Transport and Integration; Vehicle Diagnostics; Vehicle Prognostics; Vehicle mission Planning; Automated Repair and Replacement; Vehicle Control; Human Computer Interface; and Onboard Verification and Validation. Furthermore, the presented framework provides complete vehicle management which not only allows for increased crew safety and mission success through new intelligence capabilities, but also yields a mechanism for more efficient vehicle operations. The representative IVHM technologies for computer platform using heterogeneous communication, 3) coupled electromagnetic oscillators for enhanced communications, 4) Linux-based real-time systems, 5) genetic algorithms, 6) Bayesian Networks, 7) evolutionary algorithms, 8) dynamic systems control modeling, and 9) advanced sensing capabilities. This paper presents IVHM technologies developed under NASA's NFFP pilot project and the integration of these technologies forms the framework for IIVM.
This study was conducted to investigate the effects of certain factors on the impedance signal in structural health monitoring. These factors were: the quality of the bond between the sensor and the host structure, and the characteristics of the host structure, such as geometry, mass, and material properties. This work was carried out to answer a set of questions, related to these factors, that were developed by the project team. The project team was comprised of Dr. Doug Ramers and Dr. Abdul Jalloh of the Summer Faculty Fellowship Program, Mr. Arnaldo Colon- Perez, a student intern from the University of Puerto Rico of Turabo, and Mr. John Lassiter and Mr. Bob Engberg of the Structural and Dynamics Test Group at NASA Marshall Space Flight Center (MSFC). This study was based on a review of the literature on structural health monitoring to investigate the factors referred to above because there was not enough time to plan and conduct the appropriate tests at MSFC during the tenure of the Summer Faculty Fellowship Program project members. The surveyed literature documents works on structural health monitoring that were based on laboratory tests that were conducted using bolted trusses and other civil engineering type structures for the most part. These are not the typical types of structures used in designing and building NASA s space vehicles and systems. It was therefore recommended that tests be conducted using NASA type structures, such as pressure vessels, to validate the observations made in this report.
Volume IV is devoted to examining the medical and associated organizational measures used to maintain the health of space crews and to support their performance before, during, and after space flight. These measures, collectively known as the medical flight support system, are important contributors to the safety and success of space flight. The contributions of space hardware and the spacecraft environment to flight safety and mission success are covered in previous volumes of the Space Biology and Medicine series. In Volume IV, we address means of improving the reliability of people who are required to function in the unfamiliar environment of space flight as well as the importance of those who support the crew. Please note that the extensive collaboration between Russian and American teams for this volume of work resulted in a timeframe of publication longer than originally anticipated. Therefore, new research or insights may have emerged since the authors composed their chapters and references. This volume includes a list of authors' names and addresses should readers seek specifics on new information. At least three groups of factors act to perturb human physiological homeostasis during space flight. All have significant influence on health, psychological, and emotional status, tolerance, and work capacity. The first and most important of these factors is weightlessness, the most specific and radical change in the ambient environment; it causes a variety of functional and structural changes in human physiology. The second group of factors precludes the constraints associated with living in the sealed, confined environment of spacecraft. Although these factors are not unique to space flight, the limitations they entail in terms of an uncomfortable environment can diminish the well-being and performance of crewmembers in space. The third group of factors includes the occupational and social factors associated with the difficult, critical nature of the crewmembers' work: the risks involved in space flight, changes in circadian rhythms, and intragroup interactions. The physical and emotional stress and fatigue that develop under these conditions also can disturb human health and performance. In addition to these factors, the risk also exists that crewmembers will develop various illnesses during flight. The risk of illness is no less during space flight than on Earth, and may actually be greater for some classes of diseases.
Aircraft gas-turbine engine data is available from a variety of sources, including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data fusion is the integration of data or information from multiple sources for the achievement of improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/ information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This report describes a basic PHM data fusion architecture being developed in alignment with the NASA C-17 PHM Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center, NASA Dryden Flight Research Center, and Pratt & Whitney have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion, as it applies to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This report will provide a chronology and summary of the work accomplished under this research contract.
Studies have shown that reducing exposure to triggers such as air pollutants can reduce symptoms and the need for medication in asthma patients. However, systems that track asthma are generally not integrated with those that track environmental hazards related to asthma. Tlvs lack of integration hinders public health awareness and responsiveness to these environmental triggers. The current study is a collaboration between health and environmental professionals to utilize NASA-derived environmental data to develop a decision support system (DSS) for asthma prediction, surveillance, and intervention. The investigators link asthma morbidity data from the University of Mississippi Medical Center (UMMC) and Mississippi Department of Health (MDH) with air quality data from the Mississippi Department of Environmental Quality (MDEQ) and remote sensing data from NASA. Daily ambient environmental hazard data for PM2.5 and ozone are obtained from the MDEQ air quality monitoring locations and are combined with remotely sensed data from NASA to develop a state-wide spatial and time series profile of environmental air quality. These data are then used to study the correlation of these measures of air quality variation with the asthma exacerbation incidence throughout the state over time. The goal is to utilize these readily available measures to allow real-time risk assessment for asthma exacerbations. GeoMedStat, a DSS previously developed for biosurveillance, will integrate these measures to monitor, analyze and report the real-time risk assessment for asthma exacerbation throughout the state.
Integrated System Health Management (ISHM) is a capability that focuses on determining the condition (health) of every element in a complex System (detect anomalies, diagnose causes, prognosis of future anomalies), and provide data, information, and knowledge (DIaK)-not just data-to control systems for safe and effective operation. This capability is currently done by large teams of people, primarily from ground, but needs to be embedded on-board systems to a higher degree to enable NASA's new Exploration Mission (long term travel and stay in space), while increasing safety and decreasing life cycle costs of spacecraft (vehicles; platforms; bases or outposts; and ground test, launch, and processing operations). The topics related to this capability include: 1) ISHM Related News Articles; 2) ISHM Vision For Exploration; 3) Layers Representing How ISHM is Currently Performed; 4) ISHM Testbeds & Prototypes at NASA SSC; 5) ISHM Functional Capability Level (FCL); 6) ISHM Functional Capability Level (FCL) and Technology Readiness Level (TRL); 7) Core Elements: Capabilities Needed; 8) Core Elements; 9) Open Systems Architecture for Condition-Based Maintenance (OSA-CBM); 10) Core Elements: Architecture, taxonomy, and ontology (ATO) for DIaK management; 11) Core Elements: ATO for DIaK Management; 12) ISHM Architecture Physical Implementation; 13) Core Elements: Standards; 14) Systematic Implementation; 15) Sketch of Work Phasing; 16) Interrelationship Between Traditional Avionics Systems, Time Critical ISHM and Advanced ISHM; 17) Testbeds and On-Board ISHM; 18) Testbed Requirements: RETS AND ISS; 19) Sustainable Development and Validation Process; 20) Development of on-board ISHM; 21) Taxonomy/Ontology of Object Oriented Implementation; 22) ISHM Capability on the E1 Test Stand Hydraulic System; 23) Define Relationships to Embed Intelligence; 24) Intelligent Elements Physical and Virtual; 25) ISHM Testbeds and Prototypes at SSC Current Implementations; 26) Trailer-Mounted RETS; 27) Modeling and Simulation; 28) Summary ISHM Testbed Environments; 29) Data Mining - ARC; 30) Transitioning ISHM to Support NASA Missions; 31) Feature Detection Routines; 32) Sample Features Detected in SSC Test Stand Data; and 33) Health Assessment Database (DIaK Repository).