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NASA NTRS2009

An Effective Health and Medical Technical Authority

The NASA Governance model directed the formation of three Technical Authorities, Engineering; Safety and Mission Assurance; and Health and Medical, to ensure that risks are identified and adjudicated efficiently and transparently in concert with the spaceflight programs and projects. The Health and Medical Technical Authority (HMTA) has been implemented at the Johnson Space Center (JSC) and consists of the Chief Medical Office (CMO), the Deputy CMO, and HMTA Delegates. The JSC HMTA achieves the goals of risk identification and adjudication through the discharge of the appropriate technical expertise to human space flight programs and projects and the escalation of issues within program and technical authority boards. The JSC HMTA relies on subject matter experts (SMEs) in the Space Life Sciences Directorate at JSC as well as experts from other Centers to work crew health and performance issues at the technical level, develop requirements, oversee implementation and validation of requirements, and identify risks and non-compliances. Once a risk or potential noncompliance has been identified and reported to the programs or projects, the JSC HMTA begins to track it and closely monitor the program's or project's response. As a risk is developed or a non-compliance negotiated, positions from various levels of decision makers are sought at the program and project control boards. The HMTA may support a program or project position if it is satisfied with the decision making and vetting processes (ex. the subject matter expert voiced his/her concerns and all dissenting opinions were documented) and finds that the position both acknowledges the risk and cost of the mitigation and resolves the issue without changing NASA risk posture. The HMTA may disagree with a program or project position if the NASA risk posture has been elevated or obfuscated. If the HMTA does disagree with the program or project position, it will appeal to successively higher levels of authority so that risk acceptance and risk trades will be acknowledged and sanctioned at the highest appropriate level; this includes Program Managers, Mission Directorate Associate Administrators and the Agency Administrator.

Fogarty, Jennifer A.↗

NASA NTRS2010

A Collaborative Web-Based Approach to Planning Research, Integration, and Testing Using a Wiki

The National Aeronautics and Space Administration Integrated Vehicle Health Management program touches on many different research areas while striving to enable the automated detection, diagnosis, prognosis, and mitigation of adverse events at the aircraft and system level. At the system level, the research focus is on the evaluation of multidisciplinary integrated methods, tools, and technologies for achieving the program goal. The participating program members form a diverse group of government, industry, and academic researchers. The program team developed the Research and Test Integration Plan in order to track significant test and evaluation activities, which are important for understanding, demonstrating, and communicating the overall project state and project direction. The Plan is a living document, which allows the project team the flexibility to construct conceptual test scenarios and to track project resources. The Plan also incorporates several desirable feature requirements for Plan users and maintainers. A wiki has proven to be the most efficient and effective means of implementing the feature requirements for the Plan. The wiki has proven very valuable as a research project management tool, and there are plans to expand its scope.

Delaney, Michael M.↗

NASA NTRS2014

Analysis and Prediction of Weather Impacted Ground Stop Operations

When the air traffic demand is expected to exceed the available airport's capacity for a short period of time, Ground Stop (GS) operations are implemented by Federal Aviation Administration (FAA) Traffic Flow Management (TFM). The GS requires departing aircraft meeting specific criteria to remain on the ground to achieve reduced demands at the constrained destination airport until the end of the GS. This paper provides a high-level overview of the statistical distributions as well as causal factors for the GSs at the major airports in the United States. The GS's character, the weather impact on GSs, GS variations with delays, and the interaction between GSs and Ground Delay Programs (GDPs) at Newark Liberty International Airport (EWR) are investigated. The machine learning methods are used to generate classification models that map the historical airport weather forecast, schedule traffic, and other airport conditions to implemented GS/GDP operations and the models are evaluated using the cross-validations. This modeling approach produced promising results as it yielded an 85% overall classification accuracy to distinguish the implemented GS days from the normal days without GS and GDP operations and a 71% accuracy to differentiate the GS and GDP implemented days from the GDP only days.

Analysis↗

NASA NTRS2017

Cross-Cutting Risk Framework: Mining Data for Common Risks Across the Portfolio

The National Aeronautics and Space Administration (NASA) defines risk management as an integrated framework, combining risk-informed decision making and continuous risk management to foster forward-thinking and decision making from an integrated risk perspective. Therefore, decision makers must have access to risks outside of their own project to gain the knowledge that provides the integrated risk perspective. Through the Goddard Space Flight Center (GSFC) Flight Projects Directorate (FPD) Business Change Initiative (BCI), risks were integrated into one repository to facilitate access to risk data between projects. With the centralized repository, communications between the FPD, project managers, and risk managers improved and GSFC created the cross-cutting risk framework (CCRF) team. The creation of the consolidated risk repository, in parallel with the initiation of monthly FPD risk managers and risk governance board meetings, are now providing a complete risk management picture spanning the entire directorate. This paper will describe the challenges, methodologies, tools, and techniques used to develop the CCRF, and the lessons learned as the team collectively worked to identify risks that FPD programs projects had in common, both past and present.

risk-informed decision making↗

NASA NTRS2021

A Machine Learning Approach to Predict Aircraft Landing Times using Mediated Predictions from Existing Systems

We developed a novel approach for predicting the landing time of airborne flights in real-time operations. The first step predicts a landing time by using mediation rules to select from among physics-based predictions (relying on the expected flight trajectory) already available in real time in the Federal Aviation Administration System Wide Information Management system data feeds. The second step uses a machine learning model built upon the mediated predictions. The model is trained to predict the error in the mediated prediction, using features describing the current state of an airborne flight. These features are calculated in real time from a relatively small number of data elements that are readily available for airborne flights. Initial results based on five months of data at six large airports demonstrate that incorporating a machine learning model on top of the mediated physics-based prediction can lead to substantial additional improvements in prediction quality.

Machine learning↗

NASA NTRS2021