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Experimental and Analytical Development of a Health Management System for Electro-Mechanical Actuators
data.nasa.gov | Last Updated 2020-01-29T01:49:29.000ZExpanded deployment of Electro-Mechanical Actuators (EMAs) in critical applications has created much interest in EMA Prognostic Health Management (PHM), a key enabling technology of Condition Based Maintenance (CBM). As such, Impact Technologies, LLC is collaborating with the NASA Ames Research Center to perform a number of research efforts in support of NASA’s Integrated Vehicle Health Management (IVHM) initiatives. These efforts have combined experimental test stand development, laboratory seeded fault testing, and physical model-based health monitoring in a comprehensive PHM system development strategy. This paper discusses two closely related EMA research programs being conducted by Impact and NASA Ames. The first of these efforts resulted in the creation of an electro-mechanical actuator test stand for the Prognostics Center of Excellence at the NASA Ames Research Center. The second effort is ongoing and is utilizing physics-based modeling techniques to develop an algorithm and software package toolset for PHM of aircraft EMA systems using a hybrid (virtual sensor) approach.
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Airborne Electro-Mechanical Actuator Test Stand for Development of Prognostic Health Management Systems
data.nasa.gov | Last Updated 2020-01-29T04:05:01.000ZWith the advent of the next generation of aerospace systems equipped with fly-by-wire controls, electro- mechanical actuators (EMA) are quickly becoming components critical to safety of aerospace vehicles. Being relatively new to the field, however, EMA lack the knowledge base compared to what is accumulated for the more traditional actuator types, especially when it comes to fault detection and prognosis. Scarcity of health monitoring data from fielded systems and prohibitive costs of carrying out real flight tests create the need to build high-fidelity system models and design affordable yet realistic experimental setups. The objective of this work is to build an EMA test stand that, unlike current laboratory stands typically weighing in excess of one metric ton, is portable enough to be easily placed aboard a wide variety of aircraft. This stand, named the FLEA (for Flyable Electro- mechanical Actuator test stand), allows testing EMA fault detection and prognosis technologies in flight environment, thus substantially increasing their technology readiness level – all without the expense of dedicated flights, as the stand is designed to function as a non-intrusive secondary payload. No aircraft modifications are required and data can be collected during any available flight opportunity: pilot currency flights, ferry flights, or flights dedicated to other experiments. The stand is currently equipped with a prototype version of NASA Ames developed prognostic health management system with models aimed at detecting and tracking several fault types. At this point the team has completed test flights of the stand on US Air Force C-17 aircraft and US Army UH-60 helicopters and more experiments, both laboratory and airborne, are planned for the coming months.
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Impacts of Climate Variability on Primary Productivity and Carbon Distributions in the Middle Atlantic Bight and Gulf of Maine (CliVEC)
data.nasa.gov | Last Updated 2023-04-17T13:04:40.000ZTitle: The Impacts of Climate Variability on Primary Productivity and Carbon Distributions in the Middle Atlantic Bight and Gulf of Maine (CliVEC)Research Team:* Antonio Mannino (PI) - NASA GSFC* Michael Novak - NASA GSFC* Margaret Mulholland (co-PI) - Old Dominion University* Peter Bernhardt - Old Dominion University* CJ Staryk - Old Dominion University* Kimberly Hyde (co-PI) - NOAA NEFSC* Jon Hare (collaborator) - NOAA NEFSC* David Lary (co-I) - University of Texas at DallasObservations from the MODIS and SeaWiFS time series (1997-2012) and measurements from an extensive field campaign are employed to examine how inter-annual and decadal-scale climate variability affects primary productivity and organic carbon distributions along the continental margin of the U.S. northeast coast. Estimates of daily primary productivity (PP) will be computed using the Ocean Productivity from Absorption of Light (OPAL) model. OPAL vertically resolves phytoplankton absorption of photosynthetically active radiation (PAR) and relates the chlorophyll-specific absorption coefficient to sea-surface temperature (SST), where SST is a proxy for seasonal changes in the phytoplankton community. OPAL will be validated with new field measurements of PP including dissolved organic carbon production.Field measurements of particulate (POC) and dissolved organic carbon (DOC) and the absorption coefficients of phytoplankton (aph) and colored dissolved organic matter (aCDOM) will allow us to extend the validation range (temporally and spatially) for our coastal algorithms and reduce the uncertainties in satellite-derived estimates of OPAL PP, POC, DOC, aph and aCDOM. Furthermore, we will apply our extensive field data to derive region-independent ocean color algorithms for PP, POC, DOC aCDOM and aph using machine learning approaches. We will rigorously validate and compare band-ratio and multivariate machine learning algorithms. Algorithms validated from this study will be applied to satellite observations to produce a time series of satellite data productsThe U.S. Middle Atlantic Bight (MAB), George's Bank (GB) and Gulf of Maine (GoM) stand at the crossroads between major ocean circulation features - the Gulf Stream and Labrador slope-sea and shelf currents - and are influenced by highly variable river discharge, summer upwelling, warm core rings, and intense seasonal stratification. Our work will focus on the impacts of variable river discharge, SST and large-scale climate indices on primary production, and POC and DOC distributions. These processes are not unique to the MAB and GoM. Consequently, the results from this activity can be applied to understanding how inter-annual and long-term variability in climate patterns can impact the carbon cycle of continental margins throughout the globe.
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PADMINI: A PEER-TO-PEER DISTRIBUTED ASTRONOMY DATA MINING SYSTEM AND A CASE STUDY
data.nasa.gov | Last Updated 2020-01-29T04:18:17.000ZPADMINI: A PEER-TO-PEER DISTRIBUTED ASTRONOMY DATA MINING SYSTEM AND A CASE STUDY TUSHAR MAHULE*, KIRK BORNE**, SANDIPAN DEY*, SUGANDHA ARORA*, AND HILLOL KARGUPTA*** Abstract. Peer-to-Peer (P2P) networks are appealing for astronomy data mining from virtual observatories because of the large volume of the data, compute-intensive tasks, potentially large number of users, and distributed nature of the data analysis process. This paper offers a brief overview of PADMINI—a Peer-to-Peer Astronomy Data MINIng system. It also presents a case study on PADMINI for distributed outlier detection using astronomy data. PADMINI is a webbased system powered by Google Sky and distributed data mining algorithms that run on a collection of computing nodes. This paper offers a case study of the PADMINI evaluating the architecture and the performance of the overall system. Detailed experimental results are presented in order to document the utility and scalability of the system.
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Transformational Tools and Technologies Project
data.nasa.gov | Last Updated 2020-01-29T05:00:40.000Z<p>The Transformational Tools and Technologies (TTT) Project advances state-of-the-art computational and experimental tools and technologies that are vital to aviation applications in the six strategic thrusts. The project develops new computer-based tools, computational fluid dynamics models, and associated scientific knowledge that will provide first-of-a-kind capabilities to analyze, understand, and predict aviation concept performance. These revolutionary tools will be applied to accelerate NASA&rsquo;s research and the community&rsquo;s design and introduction of advanced concepts. The Project also explores technologies that are broadly-critical to advancing ARMD strategic outcomes.&nbsp; Such technologies include the understanding of new types of strong and lightweight materials, innovative controls techniques, and experimental methods.&nbsp; TTT also develops improved MDAO and systems analysis tools to enable multi-disciplinary integration. All of these technologies will support and enable concept development and benefits assessment across multiple ARMD programs and disciplines.</p><p>&nbsp;</p><p>The tools and technologies of interest span many disciplines.&nbsp; The Fluid Mechanics Discipline encompasses advanced turbulence modeling, boundary layer transition prediction and modeling, numerical methods, and flow control development and prediction for a wide range of airframe and propulsion system flow problems of interest.&nbsp; Canonical data is developed and used to validate the modeling improvements developed in this discipline.&nbsp; Development of more accurate physics-based methods such as large eddy simulation (LES) is emphasized.</p><p>The Structures and Materials Discipline emphasizes improved multifunctional and high temperature materials for airframe and engine application, as well as modeling and simulation tool development to improve validated first-principles materials and structural modeling.&nbsp; Development of ceramic matrix composite (CMC) materials for high-temperature engine application is of particular emphasis in the discipline.</p><p>The MDAO (Multi-Disciplinary Design, Analysis &amp; Optimization) and Systems Analysis Discipline develops MDAO and aircraft system-level tools to improve integration of discipline-based technologies and enable improved assessment of system-level benefits.&nbsp; An open-source framework is emphasized to better leverage external partners and increase interaction and benefit to the community.</p><p>The Combustion Discipline is developing more accurate physics-based models for complex multi-species reacting flows representative of aircraft engine combustors.&nbsp; This is done through a combination of high-fidelity benchmark experiments and the use of advanced unsteady turbulence modeling and large eddy simulation (LES) methods.&nbsp; Advanced concepts such as active combustion control and pressure-gain combustion cycles are also investigated.</p><p>The Controls Discipline encompasses work across aircraft flight controls and advanced propulsion controls.&nbsp; Development of technologies to enable distributed engine control systems are an area of emphasis in this discipline.</p><p>The Innovative Measurements Discipline conducts research to advance the state-of-the-art in cross-cutting sensing and measurement technologies for aircraft and propulsion systems.&nbsp; Areas of development include advanced optical measurements, enhanced sensing, and improved data acquisition.</p>
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Discovering Anomalous Aviation Safety Events Using Scalable Data Mining Algorithms
data.nasa.gov | Last Updated 2020-01-29T01:43:04.000ZThe worldwide civilian aviation system is one of the most complex dynamical systems created. Most modern commercial aircraft have onboard flight data recorders that record several hundred discrete and continuous parameters at approximately 1Hz for the entire duration of the flight. These data contain information about the flight control systems, actuators, engines, landing gear, avionics, and pilot commands. In this paper, recent advances in the development of a novel knowledge discovery process consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents are discussed. The data mining techniques include scalable multiple-kernel learning for large-scale distributed anomaly detection. A novel multivariate time-series search algorithm is used to search for signatures of discovered anomalies on massive datasets. The process can identify operationally significant events due to environmental, mechanical, and human factors issues in the high-dimensional flight operations quality assurance data. All discovered anomalies are validated by a team of independent domain experts. This novel automated knowledge discovery process is aimed at complementing the state-of-the-art human-generated exceedance-based analysis that fails to discover previously unknown aviation safety incidents. In this paper, the discovery pipeline, the methods used, and some of the significant anomalies detected on real-world commercial aviation data are discussed.
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A Model-based Avionic Prognostic Reasoner (MAPR)
data.nasa.gov | Last Updated 2020-01-29T04:02:06.000ZThe Model-based Avionic Prognostic Reasoner (MAPR) presented in this paper is an innovative solution for non-intrusively monitoring the state of health (SoH) and predicting the remaining useful life (RUL) of electronic and electromechanical assets by accessing and processing data obtained from a standard avionics data bus. To support Integrated Vehicle Health Monitoring (IVHM) initiatives, the solution being described here has been designed to be as non-intrusive as possible. An innovative, model-driven anomaly diagnostic and fault characterization system for electromechanical actuator (EMA) systems was developed to mitigate potentially catastrophic faults. EMA systems are used in a wide variety of aircraft applications to control critical components such as control surfaces, landing gear and thrust vector control. Failure in any one of these systems can compromise passenger safety, as well as mission success. A MIL-STD-1553 bus interface and monitor were designed to extract environmental (e.g., altitude, air speed, air density) and operational (i.e., response of system to a commanded change) data of a representative EMA system and to determine whether an anomaly is detected, and the corresponding severity. The MIL-STD-1553 bus was chosen as the test bed to develop this approach, due to its large installed base and availability of compatible development tools. Advanced and unique reasoning methodologies are applied to the extracted data sets to provide anomaly detection and fault classification on various fault modes and eventually yield SoH and RUL. In this paper we describe a data monitoring unit that will, in real time, identify, isolate, and characterize faults and establish their severity so that major performance problems can be alleviated. When built, this system will consist of a laptop with a Peripheral Component Interconnect (PCI) card slot that can accept multiple interfaces to the MAPR software package. The MAPR package will be designed to be adaptable for a large number of different platforms, for portability and for maximum input data type flexibility. This paper describes a ground-based prototype of the technology to show the efficacy of the method.
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Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks
data.nasa.gov | Last Updated 2020-01-29T04:22:00.000ZThis paper proposes a scalable, local privacy preserving algorithm for distributed Peer-to-Peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and it is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P web mining application. The proposed optimization based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive, the proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacy-preserving clustering, frequent itemset mining, and statistical aggregate computation.
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A Systems Engineering Approach to Electro-Mechanical Actuator Diagnostic and Prognostic Development
data.nasa.gov | Last Updated 2020-01-29T03:31:10.000ZThe authors have formulated a Comprehensive Systems Engineering approach to Electro-Mechanical Actuator (EMA) Prognostics and Health Management (PHM) system development. The approach implements software tools to integrate simulation-based design principles and dynamic failure mode and effects analysis. It also provides automated failure mode insertion and propagation analysis, PHM algorithm design and verification, full dynamic simulations, code generation, and validation testing. This process aims to produce the appropriate fault detection and prediction algorithms needed for successful development of an EMA PHM system. As an initial use case, the developed approach was implemented to develop and validate a model-based, virtual sensor software package for landing gear EMA PHM. This effort included creation of a dynamic, component-level system model that can be used to virtually sense parameters, detect degradation, isolate probable root cause, and assess severity. This model is also used as a virtual test bed for performing fault insertion analysis to address algorithm development and experimental prioritization. The developed model was validated using data from a test stand, which was specifically constructed for EMA PHM development. The model-based predictor was then coupled with failure mode diagnostics, advanced knowledge fusion, and failure mode progression algorithms to form a complete prototype EMA PHM solution. Reproduced by kind permission of MFPT (www.mfpt.org).
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Machine Learning for Earth Observation Flight Planning Optimization
data.nasa.gov | Last Updated 2020-01-29T03:55:45.000ZThis paper is a progress report of an effort whose goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advancedtechniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. This paper describes the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.