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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Automated System Checkout to Support Predictive Maintenance for the Reusable Launch Vehicle

The Propulsion Checkout and Control System (PCCS) is a predictive maintenance software system. The real-time checkout procedures and diagnostics are designed to detect components that need maintenance based on their condition, rather than using more conventional approaches such as scheduled or reliability centered maintenance. Predictive maintenance can reduce turn-around time and cost and increase safety as compared to conventional maintenance approaches. Real-time sensor validation, limit checking, statistical anomaly detection, and failure prediction based on simulation models are employed. Multi-signal models, useful for testability analysis during system design, are used during the operational phase to detect and isolate degraded or failed components. The TEAMS-RT real-time diagnostic engine was developed to utilize the multi-signal models by Qualtech Systems, Inc. Capability of predicting the maintenance condition was successfully demonstrated with a variety of data, from simulation to actual operation on the Integrated Propulsion Technology Demonstrator (IPTD) at Marshall Space Flight Center (MSFC). Playback of IPTD valve actuations for feature recognition updates identified an otherwise undetectable Main Propulsion System 12 inch prevalve degradation. The algorithms were loaded into the Propulsion Checkout and Control System for further development and are the first known application of predictive Integrated Vehicle Health Management to an operational cryogenic testbed. The software performed successfully in real-time, meeting the required performance goal of 1 second cycle time.

Patterson-Hine, Ann↗
NASA NTRS2004

Chronic Medication and or Monitoring for Long Duration Space Flight: What Should The Policy Be?

While a private individual with a chronic medical condition could perhaps justifiably claim the right to become a "space tourist" through informed consent, NASA and other International crew members are national assets, trained at government expense to fulfill a mission considered essential to national goals. The medical requirements/policy for selection and certification of crewmembers for long duration missions to the International Space Station (ISS) should address: (1) Crewmembers with chronic conditions that require regular dosing of life-sustaining medication for the purposes of secondary or tertiary prevention (i.e. tumor suppression therapy or anticoagulation). (2) Crew members with conditions that will be worsened by spaceflight exposure, placing them at additional personal health risk though not necessarily increasing mission risk (i.e. previous radiation exposure or preflight osteopenia/osteoporosis) The following policies are recommended. A crewmember should not be assigned to an International Space Station mission who: (3) Requires medications to sustain life or who has a disease, which requires medical procedures or diagnostics to monitor and treat same. (4) Has medical condition, which would prevent participation as a candidate in many scientific and medical operational research studies, as their underlying condition would confound the collected data. (5) Requires specialized medical devices for monitoring or treatment of a medical condition, including conditions that require special provisions to conduct routine required countermeasure activities. (6) Who has a disease or requires treatment for same, and for which the effects of space travel may be deleterious to the short or long term health of the individual, An evidence-based approach will be used to provide rationale for policy recommendations under these scenarios. Operational impacts of allowing these medical conditions to fly on the ISS will also be presented.

Hamilton, Douglas R.↗
NASA NTRS2020

Automated Cardiovascular Pathology Assessment using Semantic Segmentation and Ensemble Learning

Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac cine sequencing provides a basis to quantify cardiac measures such as ejection fraction. A method is presented that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification into a fully automatic processing pipeline. The initial processing element consists of a robust dilated convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The resulting comprehensive volumetric feature matrix captures diagnostic clinical procedure data and is utilized by the final processing element to model a cardiac pathology classifier. Our approach evaluated anonymized cardiac images from a training data set of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. The top average Dice index scores achieved were 0.940, 0.886, 0.849 for structure segmentation of the left ventricle (LV), myocardium and right ventricle (RV) respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using the trained model. Performance results demonstrate potential for advanced machine learning methods to deliver accurate, efficient and reproducible cardiac pathological assessment.

Semantic Segmentation↗
NASA NTRS2018

Automated Semantic Segmentation for Volumetric Cardiovascular Feature Quantification and Pathology Assessment

We present a pipeline method that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification as a fully automatic processing pipeline. The initial element consists of a 2D U-Net convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The results of the segmentation were used to compute a comprehensive volumetric feature matrix that captured diagnostic clinical procedure data and that was used to model a cardiac pathology classifier.Our approach evaluated anonymized parasternal MRI cardiac images from a database of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. We achieved top average Dice index scores of 0.939, 0.849, 0.886 for structure segmentation of the left ventricle (LV), right ventricle (RV) and myocardium respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using our trained model.

Lindsey, Tony↗
NASA NTRSDate not supplied

The Evolution of the Impact Evidence Library Methods – What We Learned

During fiscal years 2020-2022 NASA’s Human Research Program, Exploration Medical Capability Element has worked to source this evidence for the model. During the process several lessons learned have been identified and drive recommendations for future efforts. The ICL 1.0: Spaceflight Medical Operational expertise should be consulted regarding the addition of future conditions. A clinician experienced with the nuances of spaceflight is recommended to have a minimum of 20 hours per condition to adequately source the evidence. Condition definitions should be carefully crafted to maintain mutual exclusivity. Common side effects from medications, common and consequential incidental findings in laboratory studies, and consequential side effects of diagnostic and procedural capabilities should be considered for inclusion as unique conditions in future iterations of the ICL 1.0. Incidence: Spaceflight incidence, although based on small population sizes, is the gold standard. Terrestrial incidence should be applied cautiously. Probability of minor vs. severe cases of the condition: Data informing severity probability of the medical condition are nearly always terrestrial. Special attention should be paid to this metric as it drives outcomes in the model and terrestrial data often overestimate severity of expected cases in spaceflight. Condition duration: Three clinical phases currently exist in the model. They represent the time necessary for diagnosis, treatment/convalescence/recurrence, and the remaining duration of the mission after maximal recovery, respectively. Further subdivision of acute and convalescent treatment/recurrence is recommended. Probability of need for evacuation: Surrogates for “Return to Definitive Care” were chosen prior to data collection. In most cases these surrogates were “need for hospitalization” or “need for surgery.” For many conditions these may not be realistic drivers of “Return to Definitive Care” and should be re-examined. Probability of Crew Mortality: Terrestrial mortality data is difficult to apply to astronauts who, invariably, have less comorbidities than terrestrial subjects. It must be applied judiciously. The proportion of mission tasks impaired by the condition: “Task Impairment” was assigned to each condition prior to the condition duration data becoming available. Future efforts should assign Task Impairment as the final step in sourcing the evidence.

A.J. Kreykes↗
NASA NTRSDate not supplied

Crew Medical Training to Progressively Enable EIMO

Background. Onboard medical capabilities have greatly expanded over the history of the US space program. Newly identified space-related medical conditions, technological advances, and longer mission durations have led to an increasing need for on-demand medical expertise. Lengthy communications delays, lack of resupply and evacuation opportunities on exploration-class missions place an ever-increasing burden on the crew to provide medical care. Having adequate knowledge, skills, and abilities (KSA) available is an essential component of successful Earth Independent Medical Operations (EIMO). Without appropriate crew training and KSA, cutting-edge medical equipment has little value. Presumably, the crew will include a qualified physician; however, if the physician is incapacitated, a non-physician crew medical officer (CMO) will be needed. While more crew time is needed for medical training, there will be concomitant increases in preflight training demands for vehicle system management, operations, science, and contingencies. In truly independent operations, onboard resources such as just-in-time training, mixed reality, decision support tools, and AI-enabled chatbot “consultants” will be needed to augment KSA. Overview. Because of crew time constraints, topical priorities must be determined for preflight training. Curricula should be developed that emphasize management of conditions with relatively high incidence and morbidity/mortality. Defining the required KSA levels to treat each condition is essential, but all crewmembers should have basic lifesaving skills. Procedural and diagnostic training on live patients and simulators should be prioritized over classroom lectures. Crews must be trained with onboard equipment, resources, mixed reality, and AI-based decision support tools. Mission simulations should include medical problems with/without ground support and with appropriate communication delays. Certification guidelines for each level of KSA must be established. Skills rapidly decay for non-physician CMO’s; both pre-flight and in-flight refresher training will be needed. During spaceflight just-in-time training, simulations, and onboard CME with crew physician can help retain skills. Discussion. Medical technology, simulation design, mixed reality, and AI are advancing at a dizzying rate. Recognizing the severe constraints on crew time, it is critical that astronaut training is highly efficient and adapted to keep pace with new innovations both pre-flight and during exploration missions. These challenges will be discussed during this panel session.

Jay Lemery↗
NASA NTRS2019

Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.

US Army collaboration↗
NASA NTRS2018

Diagnostic for Verifying the Thrust Vector Requirement of the AEPS Hall-Effect Thruster and Comparison to the NEXT-C Thrust Vector Diagnostic

A diagnostic has been designed and fabricated to verify the thrust vector requirement for the Advanced Electric Propulsion System (AEPS) Hall Effect Rocket with Magnetic Shielding (HERMeS) thruster. This diagnostic will be used to verify that the propulsion system thrust vector offset from the mounting surface normal vector does not exceed 1.5 degrees over the entire throttling range and over the course of 23,000 hours of thruster testing. The diagnostic will also not violate the thruster's required voltage standoff capability in the presence of carbon backsputter by being minimally intrusive and not significantly adding to the facility backsputtered rate. Based on these requirements, an appropriate diagnostic design was determined to comprise of an array of 23 Faraday probes swept through the plume in an arc 1m from the thruster to map the beam current density. The beam current density centroid of the plume is assumed to track the thrust vector within an acceptable level of uncertainty. Additionally, a reference system, including optical alignment to the mounting surface normal vector and tilt sensors, was devised to periodically calibrate the probe position and motion throughout the long duration wear test campaign. Initial measurements of the thruster plume have been acquired to demonstrate the diagnostics functionality, verify procedures, and assess any necessary improvements prior to implementation of the diagnostic during the AEPS Engineering Development Unit (EDU) long duration wear test. To illustrate the merits of differing approaches to thrust vector determination for different classes of electric propulsion thrusters, NASA's Evolutionary Xenon Thruster-Commercial (NEXT-C) thrust vector diagnostic design details and recent data are also discussed (Appendix A).

Benavides, Gabriel F.↗
NASA NTRS2018

Diagnostic for Verifying the Thrust Vector Requirement of the AEPS Hall-Effect Thruster and Comparison to the NEXT-C Thrust Vector Diagnostic

A diagnostic has been designed and fabricated to verify the thrust vector requirement for the Advanced Electric Propulsion System (AEPS) Hall Effect Rocket with Magnetic Shielding (HERMeS) thruster. This diagnostic will be used to verify that the propulsion system thrust vector offset from the mounting surface normal vector does not exceed 1.5 degrees over the entire throttling range and over the course of 23,000 hours of thruster testing. The diagnostic will also not violate the thruster's required voltage standoff capability in the presence of carbon backsputter by being minimally intrusive and not significantly adding to the facility backsputtered rate. Based on these requirements, an appropriate diagnostic design was determined to comprise of an array of 23 Faraday probes swept through the plume in an arc 1m from the thruster to map the beam current density. The beam current density centroid of the plume is assumed to track the thrust vector within an acceptable level of uncertainty. Additionally, a reference system, including optical alignment to the mounting surface normal vector and tilt sensors, was devised to periodically calibrate the probe position and motion throughout the long duration wear test campaign. Initial measurements of the thruster plume have been acquired to demonstrate the diagnostics functionality, verify procedures, and assess any necessary improvements prior to implementation of the diagnostic during the AEPS Engineering Development Unit (EDU) long duration wear test. To illustrate the merits of differing approaches to thrust vector determination for different classes of electric propulsion thrusters, NASA's Evolutionary Xenon Thruster-Commercial (NEXT-C) thrust vector diagnostic design details and recent data are also discussed (Appendix A).

Benavides, Gabriel F.↗
NASA NTRS2018

Diagnostic for Verifying the Thrust Vector Requirement of the AEPS Hall-Effect Thruster and Comparison to the NEXT-C Thrust Vector Diagnostic

A diagnostic has been designed and fabricated to verify the thrust vector requirement for the Advanced Electric Propulsion System (AEPS) Hall Effect Rocket with Magnetic Shielding (HERMeS) thruster. This diagnostic will be used to verify that the propulsion system thrust vector offset from the mounting surface normal vector does not exceed 1.5 degrees over the entire throttling range and over the course of 23,000 hours of thruster testing. The diagnostic will also not violate the thruster's required voltage standoff capability in the presence of carbon backsputter by being minimally intrusive and not significantly adding to the facility backsputtered rate. Based on these requirements, an appropriate diagnostic design was determined to comprise of an array of 23 Faraday probes swept through the plume in an arc 1m from the thruster to map the beam current density. The beam current density centroid of the plume is assumed to track the thrust vector within an acceptable level of uncertainty. Additionally, a reference system, including optical alignment to the mounting surface normal vector and tilt sensors, was devised to periodically calibrate the probe position and motion throughout the long duration wear test campaign. Initial measurements of the thruster plume have been acquired to demonstrate the diagnostics functionality, verify procedures, and assess any necessary improvements prior to implementation of the diagnostic during the AEPS Engineering Development Unit (EDU) long duration wear test. To illustrate the merits of differing approaches to thrust vector determination for different classes of electric propulsion thrusters, NASA's Evolutionary Xenon Thruster-Commercial (NEXT-C) thrust vector diagnostic design details and recent data are also discussed (Appendix A).

Advanced Electric Propulsion System (AEPS)↗
NASA NTRS2016

Clinical Outcome Metrics for Optimization of Robust Training

Introduction: The emphasis of this research is on the Human Research Program (HRP) Exploration Medical Capability's (ExMC) "Risk of Unacceptable Health and Mission Outcomes Due to Limitations of In-Flight Medical Capabilities." Specifically, this project aims to contribute to the closure of gap ExMC 2.02: We do not know how the inclusion of a physician crew medical officer quantitatively impacts clinical outcomes during exploration missions. The experiments are specifically designed to address clinical outcome differences between physician and non-physician cohorts in both near-term and longer-term (mission impacting) outcomes. Methods: Medical simulations will systematically compare success of individual diagnostic and therapeutic procedure simulations performed by physician and non-physician crew medical officer (CMO) analogs using clearly defined short-term (individual procedure) outcome metrics. In the subsequent step of the project, the procedure simulation outcomes will be used as input to a modified version of the NASA Integrated Medical Model (IMM) to analyze the effect of the outcome (degree of success) of individual procedures (including successful, imperfectly performed, and failed procedures) on overall long-term clinical outcomes and the consequent mission impacts. The procedures to be simulated are endotracheal intubation, fundoscopic examination, kidney/urinary ultrasound, ultrasound-guided intravenous catheter insertion, and a differential diagnosis exercise. Multiple assessment techniques will be used, centered on medical procedure simulation studies occurring at 3, 6, and 12 months after initial training (as depicted in the following flow diagram of the experiment design). Discussion: Analysis of procedure outcomes in the physician and non-physician groups and their subsets (tested at different elapsed times post training) will allow the team to 1) define differences between physician and non-physician CMOs in terms of both procedure performance (pre-IMM analysis) and overall mitigation of the mission medical impact (IMM analysis); 2) refine the procedure outcome and clinical outcome metrics themselves; 3) refine or develop innovative medical training products and solutions to maximize CMO performance; and 4) validate the methods and products of this experiment for operational use in the planning, execution, and quality assurance of the CMO training process The team has finalized training protocols and developed a software training/testing tool in collaboration with Butler Graphics (Detroit, MI). In addition to the "hands on" medical procedure modules, the software includes a differential diagnosis exercise (limited clinical decision support tool) to evaluate the diagnostic skills of participants. Human subject testing will occur over the next year.

Ebert, D.↗
NASA NTRS1994

Laboratory simulation of Low Earth Orbit (LEO) atomic oxygen effects

A pulsed fast oxygen atom source has been used extensively over the last 7 years to investigate the effects of ambient oxygen atoms impacting materials placed in low Earth orbit. In this period, we irradiated well over 2000 material samples with 8 km/s oxygen atoms generated in our source. Typical irradiance level is 3 x 10(exp 20) O atoms/sq cm although some materials have been irradiated to fluence levels as high as 6 x 10(exp 21) O atoms/sq cm. The operating principles and characteristics of our source are reviewed along with diagnostic and handling procedures appropriate to material testing. Representative data is presented on the velocity dependence of oxygen atom erosion rates (the PSI source provides oxygen atoms tunable over the velocity range of 5 to 12 km/s) as well as the dependence on material temperature. Specific examples of non-linear oxidative effects related to surface contamination and test duration are also be provided.

Caledonia, George E.↗
NASA NTRS1991

Diagnostic evaluation of numerical model simulations using the tendency equation

A procedure is proposed to expand the diagnostic capabilities of the pressure tendency equation of a primitive equation NWP model by computing the pressure tendency in physical coordinates. The advantages of isolating the density advection as a diagnostic tool to understand pressure changes is shown. By simple thermodynamic arguments it is demonstrated that in areas of synoptic-scale cyclonic development, the vertically integrated density advection is more than sufficient to explain the depletion of mass over a growing depression. Consequently, the joint contribution of the net divergence and vertical motion opposes the pressure fall. This is illustrated for a case of rapid cyclogenesis in southern South America.

Jusem, Juan C.↗
NASA NTRS1989

A diagnostic for determining the quality of single-reference electron correlation methods

It was recently proposed that the Euclidian norm of the t sub 1 vector of the coupled cluster wave function (normalized by the number of electrons included in the correlation procedure) could be used to determine whether a single-reference-based electron correlation procedure is appropriate. This diagnostic, T sub 1, is defined for use with self consistent field molecular orbitals and is invariant to the same orbital rotations as the coupled cluster energy. T sub 1 is investigated for several different chemical systems which exhibit a range of multireference behavior, and is shown to be an excellent measure of the importance of nondynamical electron correlation and is far superior to C sub 0 from a singles and doubles configuration interaction wave function. It is further suggested that when the aim is to recover a large fraction of the dynamical electron correlation energy, a large T sub 1 (i.e., greater than 0.02) probably indicates the need for a multireference electron correlation procedure.

Lee, Timothy J.↗
NASA NTRS1989

A diagnostic for determining the quality of single-reference electron correlation methods

It was recently proposed that the Euclidian norm of the t(sub 1) vector of the coupled cluster wave function (normalized by the number of electrons included in the correlation procedure) could be used to determine whether a single-reference-based electron correlation procedure is appopriate. This diagnostic, T(sub 1) is defined for use with self-consistent-field molecular orbitals and is invariant to the same orbital rotations as the coupled cluster energy. T(sub 1) is investigated for several different chemical systems which exhibit a range of multireference behavior, and is shown to be an excellent measure of the importance of non-dynamical electron correlation and is far superior to C(sub 0) from a singles and doubles configuration interaction wave function. It is further suggested that when the aim is to recover a large fraction of the dynamical electron correlation energy, a large T(sub 1) (i.e., greater than 0.02) probably indicates the need for a multireference electron correlation procedure.

Lee, Timothy J.↗
NASA NTRS1971

IMBLMS phase B4, additional tasks 5.0. Microbial identification system

A laboratory study was undertaken to provide simplified procedures leading to the presumptive identification (I/D) of defined microorganisms on-board an orbiting spacecraft. Identifications were to be initiated by nonprofessional bacteriologists, (crew members) on a contingency basis only. Key objectives/constraints for this investigation were as follows:(1) I/D procedures based on limited, defined diagnostic tests, (2) testing oriented about ten selected microorganisms, (3) provide for definitive I/D key and procedures per selected organism, (4) define possible occurrences of false positives for the resulting I/D key by search of the appropriate literature, and (5) evaluation of the I/D key and procedure through a limited field trial on randomly selected subjects using the I/D key.

Source record↗
NASA NTRS1992

A test suite for magnetohydrodynamical simulations

A collection is presented of MHD problems that will provide researchers who have interests in modeling MHD phenomena with a battery of tests (a 'test suite') for calibrating their numerical algorithms. Use of these tests will provide a common reference for comparison of different numerical MHD algorithms. The test suite includes both 1D and 2D problems. Taken together, these problems test the abilities of a numerical method to propagate accurately all of the MHD wave families in moving and stationary media, to capture MHD shocks and contact discontinuities, and to model accurately Lorentz force terms in multiple dimensions. An example solution of each test problem is presented. Diagnostic convergence-testing procedures that provide a quantitative evaluation of MHD algorithms are demonstrated.

Stone, James M.↗