- API
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.
- API
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.
- API
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.
- API
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.
- API
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.
- API
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.
- API
SIAM 2007 Text Mining Competition dataset
data.nasa.gov | Last Updated 2020-01-29T04:25:03.000Z**Subject Area:** Text Mining **Description:** This is the dataset used for the SIAM 2007 Text Mining competition. This competition focused on developing text mining algorithms for document classification. The documents in question were aviation safety reports that documented one or more problems that occurred during certain flights. The goal was to label the documents with respect to the types of problems that were described. This is a subset of the Aviation Safety Reporting System (ASRS) dataset, which is publicly available. **How Data Was Acquired:** The data for this competition came from human generated reports on incidents that occurred during a flight. **Sample Rates, Parameter Description, and Format:** There is one document per incident. The datasets are in raw text format. All documents for each set will be contained in a single file. Each row in this file corresponds to a single document. The first characters on each line of the file are the document number and a tilde separats the document number from the text itself. **Anomalies/Faults:** This is a document category classification problem.
- API
IceCube Level 1 Radiance Data and Codes
data.nasa.gov | Last Updated 2023-01-31T14:55:30.000ZThis zipped meta data file can be expanded into two folders. One folder contains the daily calibrated Level 1 radiance and geolocation data in HDF5 format, and the other folder contains the main IDL codes that process the data and make plots (mainly for generating plots for the paper Gong et al. 2021 that is under review for Earth Science System Data journal). Both folders contain a README file in each to guide readers through the file name, variable namelist, quality flag, code's usage, etc.
- API
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.
- API
OMPS/NPP PCA SO2 Total Column 1-Orbit L2 Swath 50x50km V2 (OMPS_NPP_NMSO2_PCA_L2) at GES DISC
data.nasa.gov | Last Updated 2022-01-17T05:47:20.000ZThe OMPS_NPP_NMSO2_PCA_L2 product is part of the MEaSUREs (Making Earth Science Data Records for Use in Research Environments) suite of products. It is retrieved from the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper (NM) spectrometer and provides contiguous daily global monitoring of anthropogenic and volcanic sulfur dioxide (SO2), an important pollutant and aerosol precursor that affects both air quality and the climate. The product is based on the NASA Goddard Space Flight Center principal component analysis (PCA) spectral fitting algorithm (Li et al., 2013, 2017), and continues (Zhang et al., 2017) NASA's Earth Observing System (EOS) standard Aura/Ozone Monitoring Instrument SO2 product (OMSO2). The latest OMPS_NPP_NMSO2_PCA_L2 V2 product uses new Jacobian lookup tables and more realistic model based a priori profiles in anthropogenic SO2 retrievals. This helps to more accurately account for the pixel-to-pixel variation in SO2 sensitivity due to different factors such as the vertical distribution of SO2, solar and viewing angles, surface reflectivity, and cloudiness. As compared with the previous OMPS_NPP_NMSO2_PCA_L2 V1.2 product that assumes the same SO2 sensitivity for all OMPS pixels, the new V2 anthropogenic SO2 retrievals have reduced retrieval biases especially over background regions (see Figure 1 for an example). The same updated PCA SO2 retrieval algorithm (Li et al., 2020) is also used to produce the recently released OMSO2 V2 product (doi:10.5067/Aura/OMI/DATA2022). The new OMPS_NPP_NMSO2_PCA_L2 V2 product thus offers enhanced consistency between the NASA EOS standard (OMI) and continuity (OMPS) SO2 data records Sulfur Dioxide (SO2) is a short-lived gas primarily produced by volcanoes, power plants, refineries, metal smelting and burning of fossil fuels. Where SO2 remains near the Earth's surface, it is toxic, causes acid rain, and degrades air quality. Where SO2 is lofted into the free troposphere, it forms aerosols that can alter cloud reflectivity and precipitation. In the stratosphere, volcanic SO2 forms sulfate aerosols that can result in climate change.