Modeling Small Unmanned Aerial System Mishaps Using Logistics Regression and Artificial Neural Networks

A dataset of 854 small unmanned aerial system (SUAS) flight experiments from 2005-2009 is analyzed to determine significant factors that contribute to mishaps. The data from 29 airframes of different designs and technology readiness levels were aggregated. 20 measured parameters from each flight experiment are investigated, including wind speed, pilot experience, number of prior flights, pilot currency, etc. Outcomes of failures (loss of flight data) and damage (injury to airframe) are classified by logistic regression modeling and artificial neural network analysis. From the analysis, it can be concluded that SUAS damage is a random event that cannot be predicted with greater accuracy than guessing. Failures can be predicted with greater accuracy (38.5% occurrence, model hit rate 69.6%). Five significant factors were identified by both the neural networks and logistic regression. SUAS prototypes risk failures at six times the odds of their commercially manufactured counterparts. Likewise, manually controlled SUAS have twice the odds of experiencing a failure as those autonomously controlled. Wind speeds, pilot experience, and pilot currency were not found to be statistically significant to flight outcomes. The implications of these results for decision makers, range safety officers and test engineers are discussed

Applications of remote sensing in hydrology

OWRT Project no. B-160-COLO

Safety and Mission Assurance Acronyms, Abbreviations, and Definitions

This NASA Technical Handbook compiles into a single volume safety, reliability, maintainability, and quality assurance and risk management terms defined and used in NASA safety and mission assurance directives and standards. The purpose of this handbook is to support effective communication within NASA and with its contractors. The definitions in this handbook are updated when the definition of the acronym or term is updated in the originating document

User definition and mission requirements for unmanned airborne platforms, revised

The airborne measurement requirements of the scientific and applications experiment user community were assessed with respect to the suitability of proposed strawman airborne platforms. These platforms provide a spectrum of measurement capabilities supporting associated mission tradeoffs such as payload weight, operating altitude, range, duration, flight profile control, deployment flexibility, quick response, and recoverability. The results of the survey are used to examine whether the development of platforms is warranted and to determine platform system requirements as well as research and technology needs

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 159

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1976

A Systems Approach to Assessing, Interpreting and Applying Human Error Mishap Data to Mitigate Risk of Future Incidents in a Space Exploration Ground Processing Operations Environment

Research results have shown that more than half of aviation, aerospace and aeronautics mishaps/incidents are attributed to human error. Although many existing incident report systems have been beneficial for identifying engineering failures, most of them are not designed around a theoretical framework of human error, thus failing to address core issues and causes of the mishaps. Therefore, it is imperative to develop a human error assessment framework to identify these causes. This research focused on identifying causes of human error and leading contributors to historical Launch Vehicle Ground Processing Operations mishaps based on past mishaps, near mishaps, and close calls. Three hypotheses were discussed. The first hypothesis addressed the impact Human Factor Analysis and Classification System (HFACS) contributing factors (unsafe acts of operators, preconditions for unsafe acts, unsafe supervision, and/or organizational influences) have on human error events (i.e. mishaps, close calls, incident or accidents) in NASA Ground Processing Operations. The second hypothesis focused on determining if the HFACS framework conceptual model could be proven to be a viable analysis and classification system to help classify both latent and active underlying contributors and causes of human error in ground processing operations. Lastly, the third hypothesis focused on determining if the development of a model using the Human Error Assessment and Reduction Technique (HEART) could be used as a tool to help determine the probability of human error occurrence in ground processing operations. A model to analyze and classify contributing factors to mishaps or incidents, and generate predicted Human Error Probabilities (HEPs) of future occurrence was developed using the HEART and HFACS tools. The research methodology was applied (retrospectively) to six Ground Processing Operations (GPO) Scenarios and 30 years of Launch Vehicle Related Mishap Data. Surveys were used to provide Subject Matter Experts\u27 (SMEs) subjective assessments of the impact Error Producing Conditions (EPC) had on specific tasks. In this research a Logistic Binary Regression model, which identified the four most significant contributing HFACS human error factors was generated. This model provided predicted probabilities of future occurrence of mishaps when these contributing factors are present. The results showed that the HEART and HFACS methods, when modified, can be used as an analysis tool to identify contributing factors, their impact on human error events, and predict the potential probability of future human error occurrence. This methodology and framework was validated through consistency and comparison to other related research. A contribution methodology for other space operations and similar complex operations to follow was provided from this research. Future research should involve broadening the scope to explore and identify other existing models of human error management systems to integrate into complex space systems beyond what was conducted in this research

Feasibility study of an Integrated Program for Aerospace vehicle Design (IPAD). Volume 1B: Concise review

Reports on the design process, support of the design process, IPAD System design catalog of IPAD technical program elements, IPAD System development and operation, and IPAD benefits and impact are concisely reviewed. The approach used to define the design is described. Major activities performed during the product development cycle are identified. The computer system requirements necessary to support the design process are given as computational requirements of the host system, technical program elements and system features. The IPAD computer system design is presented as concepts, a functional description and an organizational diagram of its major components. The cost and schedules and a three phase plan for IPAD implementation are presented. The benefits and impact of IPAD technology are discussed