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NEW HORIZONS SDC PLUTO CRUISE RAW V2.0
data.nasa.gov | Last Updated 2023-01-26T20:54:05.000ZThis data set contains Raw data taken by the New Horizons Student Dust Counter instrument during the pluto cruise mission phase. This is VERSION 2.0 of this data set. SDC collected science data intermittently during the hibernation years following the Jupiter encounter, designated as the PLUTOCRUISE phase. There were also Annual Checkouts (ACOs), STIM calibrations, Noise calibrations, and an anomaly in November, 2007. SDC's main science data collection periods were during hibernation. During ACOs, science data are taken intermittently but the user must be careful in analyzing these data since there is usually more activity on the spacecraft during hibernation. STIM and Noise refer to scheduled calibrations and are done with a regular cadence of one per year after the Jupiter encounter; they occurred sporadically in the early years of the mission. Note that some SDC data files have the same stop and start time and a zero exposure time. The reason for this is that the start and stop time for SDC data files are the event times for the first and last events in the files, so for files that contain a single event, these two values are the same. The changes in Version 2.0 were re-running of the ancillary data in the data product, updated geometry from newer SPICE kernels, minor editing of the documentation, catalogs, etc., and resolution of liens from the December, 2014 review, plus those from the May, 2016 review of the Pluto Encounter data sets. New observations added with this version (V2.0) include ongoing cruise observations from August, 2014 through January, 2015.
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PHOENIX MARS ROBOTIC ARM CAMERA 5 NORMAL OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:52:37.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains normal data from the Robotic Arm Camera (RAC).
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Classification of Aeronautics System Health and Safety Documents
data.nasa.gov | Last Updated 2020-01-29T01:57:57.000ZMost complex aerospace systems have many text reports on safety, maintenance, and associated issues. The Aviation Safety Reporting System (ASRS) spans several decades and contains over 700 000 reports. The Aviation Safety Action Plan (ASAP) contains over 12 000 reports from various airlines. Problem categorizations have been developed for both ASRS and ASAP to enable identification of system problems. However, repository volume and complexity make human analysis difficult. Multiple experts are needed, and they often disagree on classifications. Even the same person has classified the same document differently at different times due to evolving experiences. Consistent classification is necessary to support tracking trends in problem categories over time. A decision support system that performs consistent document classification quickly and over large repositories would be useful. We discuss the results of two algorithms we have developed to classify ASRS and ASAP documents. The first is Mariana---a support vector machine (SVM) with simulated annealing, which is used to optimize hyperparameters for the model. The second method is classification built on top of nonnegative matrix factorization (NMF), which attempts to find a model that represents document features that add up in various combinations to form documents. We tested both methods on ASRS and ASAP documents with the latter categorized two different ways. We illustrate the potential of NMF to provide document features that are interpretable and indicative of topics. We also briefly discuss the tool that we have incorporated Mariana into in order to allow human experts to provide feedback on the document categorizations.
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PHOENIX MARS ROBOTIC ARM CAMERA 5 XYZ OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:09:16.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains xyz data from the Robotic Arm Camera (RAC).
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NEW HORIZONS SDC PLUTO CRUISE CALIBRATED V2.0
data.nasa.gov | Last Updated 2023-01-26T20:25:34.000ZThis data set contains Calibrated data taken by the New Horizons Student Dust Counter instrument during the pluto cruise mission phase. This is VERSION 2.0 of this data set. SDC collected science data intermittently during the hibernation years following the Jupiter encounter, designated as the PLUTOCRUISE phase. There were also Annual Checkouts (ACOs), STIM calibrations, Noise calibrations, and an anomaly in November, 2007. SDC's main science data collection periods were during hibernation. During ACOs, science data are taken intermittently but the user must be careful in analyzing these data since there is usually more activity on the spacecraft during hibernation. STIM and Noise refer to scheduled calibrations and are done with a regular cadence of one per year after the Jupiter encounter; they occurred sporadically in the early years of the mission. Note that some SDC data files have the same stop and start time and a zero exposure time. The reason for this is that the start and stop time for SDC data files are the event times for the first and last events in the files, so for files that contain a single event, these two values are the same. The changes in Version 2.0 were re-running of the ancillary data in the data product, updated geometry from newer SPICE kernels, minor editing of the documentation, catalogs, etc., and resolution of liens from the December, 2014 review, plus those from the May, 2016 review of the Pluto Encounter data sets. New observations added with this version (V2.0) include ongoing cruise observations from August, 2014 through January, 2015.
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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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Metrics for Evaluating Performance of Prognostic Techniques
data.nasa.gov | Last Updated 2020-01-29T03:23:28.000ZPrognostics is an emerging concept in condition basedmaintenance(CBM)ofcriticalsystems.Alongwith developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenancehave been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.
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PHOENIX MARS ROBOTIC ARM CAMERA 5 DISPARITY OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:39:06.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains disparity data from the Robotic Arm Camera (RAC).
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PHOENIX MARS ROBOTIC ARM CAMERA 5 ROUGHNESS OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:30:16.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains roughness data from the Robotic Arm Camera (RAC).
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PHOENIX MARS ROBOTIC ARM CAMERA 4 LINEARIZED OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:23:42.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains linearized data from the Robotic Arm Camera (RAC).