An approach to the classification of meteorological satellite data.
Meteorological data classification system using computer based level logical-tree method
DATA PROCESSING↗
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Artemis I Flight Instrumentation Data Quality Assessment and Processing
This paper is in support of the SciTech 2024 Space Launch System Aerosciences Special Sessions being organized by Brent Pomeroy and Jeremy Pinier. On November 16th, 2022, NASA launched the inaugural test flight of the Space Launch System (SLS) carrying the Orion capsule into a high orbit far beyond the Moon. The launch vehicle was instrumented with over three thousand flight instrumentation sensors, which monitored aerodynamic, acoustic, structural, and thermal environments. These data are intended to validate experimental and numerical tools used to predict the design environments which the vehicle experiences during launch and ascent. Prior to launch, tests were performed at the SLS Systems Integration Laboratory (SIL) using flight-like avionics and on the integrated flight hardware of Artemis I at the Vehicle Assembly Building (VAB). The purpose of these tests was to characterize the data acquisition units (DAUs) used to record and telemeter flight data to ground stations in order to assure that flight test objectives can be achieved and to quantify the expected quality of the flight data. In addition, pre-flight assessment and development of tools and methods used to process and disseminate flight data at the Huntsville Operations Support Center (HOSC) were conducted and adjustments made with respect to DAU time-synchronization prior to and after the flight. This paper summarizes these tests and some aspects of the post processing of data are discussed.
Developmental Flight Instrumentation↗
Development of Solar Flare and Energetic Particle Prediction Portal (SEP 3 )
Solar activity is a primary factor determining the state of the Earth’s space environment, geomagnetic and ionospheric disturbances, and radiation hazards. In the current state of knowledge, machine learning (ML) methods provide essential tools for processing data, investigating relationships among various physical properties and characteristics, uncovering hidden connections, and predicting hazardous solar events. The primary difficulty in developing and applying modern machine-learning tools in heliophysics is that the essential data are scattered among over a hundred data repositories developed by instrument teams of space missions and ground-based observatories. In addition, statistical and ML methods require long time series of homogeneous measurements. To facilitate ML-ready data preparation and access, we have developed an interactive database of solar flares integrating the most essential datasets (https://solarflare.njit.edu/). The database performs an initial data processing and is automatically updated. In addition, we are developing the Solar Energetic Particle Prediction Portal (SEP3, https://sun.njit.edu/SEP3), which hosts web applications that allow users to retrieve the database records. The Portal has a search page for browsing the events from the most widely used catalogs and a dedicated space to share the most recent achievements of the team. The interactive widget can display soft X-ray and proton flux time series from GOES satellites and the flare records. The data portal has been used to evaluate the forecasts of solar proton events and investigate machine-learning approaches to SEP prediction.
SMD↗
Validation of a Probabilistic Continuity Assessment Method for Earth Observation Portfolios
With the increased reliance on spaceborne Earth observation data among the Earth science community and other end users, it is important that efforts are made to promote data continuity for a range of parameters of interest. Continuity gaps may occur between missions measuring like parameters due to mission development delays or early termination and introduce the potential of increased uncertainty for retrieved parameters. To inform portfolio-level decisions for Earth observing missions, Ivanco et al. developed a method that enables the assessment of the probability of continuity gaps for multi-mission architectures and provides a framework to assess this probability in the context of multiple scenarios that represent possible future states of the architecture [Ivanco et al., “A Scenario-Based Approach to Assess Continuity Gaps in Earth Observations,” IEEE Aerospace Conf., 2024]. While this method was previously applied to assess continuity gaps for a specific multi-mission architecture, it had not yet been validated with historical data. This paper outlines the process of validating the probabilistic gap assessment method by utilizing data obtained from past NASA Earth Science mission formulation documents and provides a discussion of the results and takeaways from the validation. Future areas of improvement pertaining to both the modeling and simulation methodologies and underlying assumptions are identified and discussed.
Katharine Burn↗
Systems and Methods for Radar Data Communication
A radar information processing system is operable to process high bandwidth radar information received from a radar system into low bandwidth radar information that may be communicated to a low bandwidth connection coupled to an electronic flight bag (EFB). An exemplary embodiment receives radar information from a radar system, the radar information communicated from the radar system at a first bandwidth; processes the received radar information into processed radar information, the processed radar information configured for communication over a connection operable at a second bandwidth, the second bandwidth lower than the first bandwidth; and communicates the radar information from a radar system, the radar information communicated from the radar system at a first bandwidth.
Bunch, Brian↗
Method Development for In-situ Detection of Latent Herpesvirus DNA from Saliva using Nanopore Sequencing
Research toward latent herpesvirus reactivation has been intensively addressed through Space Shuttle and International Space Station (ISS) investigations. This work has provided the understanding that persistent reactivation of herpesviruses from asymptomatic crew can be detected through viral shedding in saliva, urine, and blood. Occasionally, viral reactivation from the latency stage can pose a threat to crew health (clinical manifestation) before, during, and after flight missions. Furthermore, previous work detailing correlations with immunity indicate that monitoring viral reactivation could be implemented to assess potential immune dysfunction. While in-flight monitoring is desirable, there is no well-established procedure or method for real-time evaluations, and research to date has relied on postflight, ground-based analysis. The development of portable molecular technologies like the miniPCR™ (miniPCR Bio) thermal cycler and the MinION™ sequencer (Oxford Nanopore Technologies) have confirmed that real-time monitoring is possible in extreme and low resource environments. These devices, combined with simple sample preparation methods, have been used to demonstrate bacterial identification onboard the ISS, as well as rapid viral detection in remote locations on Earth. The work here builds upon previous molecular advancements onboard the ISS toward the development and validation of a spaceflight-compatible method for viral detection from crew samples. Several herpesviruses can be detected in saliva, which provides a non-invasive means to collect samples for monitoring. While the basis for this method lies in previous spaceflight investigations, key points for method optimization include DNA extraction from saliva, viral primer selection, and bioinformatic processes for data analysis. To increase viral yield, numerous DNA extraction methods have been evaluated and will be discussed in detail. For initial development and testing, varicella-zoster virus (VZV) is being targeted though open reading frame 51 and 63 (ORF51, ORF63), as the replication origin-binding protein is highly expressed during latency. Optimization of the thermal cycling parameters has resulted in the ability to test the entire process. The full method has been tested with both viral VZV DNA standards and saliva spiked with varying concentrations of VZV. Viral sequence data were mapped to the reference sequence using minimap2. Prior to mapping, DNA sequencing reads were filtered for length and quality, barcodes were removed, and alignment identity calculated. Following further assessments, statistics were compared across multiple sequencing experiments and are being used to determine the success of the protocols. Forward work will include the incorporation of herpes simplex virus 1 (HSV-1) and Epstein-Barr virus (EBV) primers as well as the validation of results to the terrestrial qPCR standard assay. Upon full validation of the developed method, saliva will be collected from 20 healthy subjects and spiked with viral DNA. These samples will be split and assayed with the MinION and standard qPCR assay.
Hang N Nguyen↗
Improving Sim-to-Real Transfer in Vision-Based Robot Navigation Via Instance-Level GAN-Based Data Augmentation
Achieving robust vision-based robotic tasks requires large amounts of data, which are often difficult to obtain in real-world scenarios. Simulators and synthetic data offer a cost-effective alternative, but the visual gap between simulation and reality hinders the performance of models when deployed in real-world environments. In this paper, we present a data augmentation pipeline that integrates a foundation model (Segment Anything Model) with an unsupervised image-to-image translation model (CycleGAN) for instance-level domain transfer from simulation to reality. This pipeline enables the generation of realistic labeled data from synthetic images for training supervised machine learning models in vision-based navigation tasks. We evaluate our approach on real-world data for ego-vehicle pose estimation, a critical autonomous navigation task involving the prediction of cross-track position and heading angle relative to road center line markings. The results of our tests show that our GAN-based data augmentation pipeline significantly outperforms models trained solely on simulation data or on data processed with standard image augmentation methods for sim-to-real transfer, enhancing model robustness and generalizability in real-world scenarios. Our method provides a scalable and flexible data augmentation tool for leveraging large synthetic datasets to enhance vision-based robotic navigation tasks.
artificial intelligence↗
Automatic processing of multispectral observations
Statistical methods for inventory boundary determination and data compression in automatic processing of multispectral scanner remote sensor earth observations from aircraft and spacecraft
Krause, F. R.↗
Observational magnetometer calibration with the Hubble Space Telescope's new magnetometers
The two magnetometers recently replaced on the Hubble Space Telescope during the STS-61 Servicing Mission are now being used successfully for Coarse Attitude Determination during spacecraft vehicle safemode recovery operation. The magnetometer alignments relative to the spacecraft's vehicle's reference frame and the magnetic coupling of the sensors to the four magnetic torquer bars were determined. Coarse Attitude determination errors are now reduced to an average of 0.6 deg. Magnetometer Sensing System calibration and Coarse Attitude determination testing with the new calibration parameters is a geometrical problem. Telemetered earth magnetic field data was collected at twenty-six different vehicle attitudes. The spacecraft attitudes selected were distributed as widely apart as possible throughout the Geocentric Inertial Coordinate reference frame. It is also desirable to sample the Earth's magnetic field over as many different locations of the spacecraft's passage over the Earth as possible, within the limitation of the +/- 28.5 deg orbital inclination. A full range of magnetic moment outputs from the torquer bars needs to be sampled, +/- 3600 ampmeters squared, as well as data when the torquer bars have zero current. Graphic utilities were also developed to visually aid in optimizing the data collection process. Finally, a brief discussion of a method for collecting data for future calibrations is suggested.
Broude, Sidney M.↗
Constructing the 'Best' Reliability Data for the Job - Developing Generic Reliability Data from Alternative Sources Early in a Product's Development Phase
Reliability practitioners advocate getting reliability involved early in a product development process. However, when assigned to estimate or assess the (potential) reliability of a product or system early in the design and development phase, they are faced with lack of reasonable models or methods for useful reliability estimation. Developing specific data is costly and time consuming. Instead, analysts rely on available data to assess reliability. Finding data relevant to the specific use and environment for any project is difficult, if not impossible. Instead, analysts attempt to develop the "best" or composite analog data to support the assessments. Industries, consortia and vendors across many areas have spent decades collecting, analyzing and tabulating fielded item and component reliability performance in terms of observed failures and operational use. This data resource provides a huge compendium of information for potential use, but can also be compartmented by industry, difficult to find out about, access, or manipulate. One method used incorporates processes for reviewing these existing data sources and identifying the available information based on similar equipment, then using that generic data to derive an analog composite. Dissimilarities in equipment descriptions, environment of intended use, quality and even failure modes impact the "best" data incorporated in an analog composite. Once developed, this composite analog data provides a "better" representation of the reliability of the equipment or component. It can be used to support early risk or reliability trade studies, or analytical models to establish the predicted reliability data points. It also establishes a baseline prior that may updated based on test data or observed operational constraints and failures, i.e., using Bayesian techniques. This tutorial presents a descriptive compilation of historical data sources across numerous industries and disciplines, along with examples of contents and data characteristics. It then presents methods for combining failure information from different sources and mathematical use of this data in early reliability estimation and analyses.
Kleinhammer, Roger K.↗
Engineering and Scientific Applications: Using MatLab(Registered Trademark) for Data Processing and Visualization
MatLab(R) (MATrix LABoratory) is a numerical computation and simulation tool that is used by thousands Scientists and Engineers in many cou ntries. MatLab does purely numerical calculations, which can be used as a glorified calculator or interpreter programming language; its re al strength is in matrix manipulations. Computer algebra functionalities are achieved within the MatLab environment using "symbolic" toolbo x. This feature is similar to computer algebra programs, provided by Maple or Mathematica to calculate with mathematical equations using s ymbolic operations. MatLab in its interpreter programming language fo rm (command interface) is similar with well known programming languag es such as C/C++, support data structures and cell arrays to define c lasses in object oriented programming. As such, MatLab is equipped with most ofthe essential constructs of a higher programming language. M atLab is packaged with an editor and debugging functionality useful t o perform analysis of large MatLab programs and find errors. We belie ve there are many ways to approach real-world problems; prescribed methods to ensure foregoing solutions are incorporated in design and ana lysis of data processing and visualization can benefit engineers and scientist in gaining wider insight in actual implementation of their perspective experiments. This presentation will focus on data processing and visualizations aspects of engineering and scientific applicati ons. Specifically, it will discuss methods and techniques to perform intermediate-level data processing covering engineering and scientifi c problems. MatLab programming techniques including reading various data files formats to produce customized publication-quality graphics, importing engineering and/or scientific data, organizing data in tabu lar format, exporting data to be used by other software programs such as Microsoft Excel, data presentation and visualization will be discussed. The presentation will emphasize creating practIcal scripts (pro grams) that extend the basic features of MatLab TOPICS mclude (1) Ma trix and vector analysis and manipulations (2) Mathematical functions (3) Symbolic calculations & functions (4) Import/export data files (5) Program lOgic and flow control (6) Writing function and passing parameters (7) Test application programs
Sen, Syamal K.↗
On-Line Robust Modal Stability Prediction using Wavelet Processing
Wavelet analysis for filtering and system identification has been used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins is reduced with parametric and nonparametric time- frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data is used to reduce the effects of external disturbances and unmodeled dynamics. Parametric estimates of modal stability are also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. The F-18 High Alpha Research Vehicle aeroservoelastic flight test data demonstrates improved robust stability prediction by extension of the stability boundary beyond the flight regime. Guidelines and computation times are presented to show the efficiency and practical aspects of these procedures for on-line implementation. Feasibility of the method is shown for processing flight data from time- varying nonstationary test points.
Brenner, Martin J.↗
Total Dissolved Nitrogen and Ammonia Data for the East River Watershed, Colorado (2015-2025)
This data package contains mean values for total dissolved nitrogen (TDN) and ammonia concentrations for water samples taken from the East River Watershed in Colorado. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. TDN was analyzed using a Shimadzu Total Nitrogen Module (TNM-1) combined with the TOC-VCSH analyzer (Shimadzu Corporation, Japan). TNM-1 is a non-specific measurement of total nitrogen (TN). All nitrogen species in samples are combusted to nitrogen monoxide and nitrogen dioxide, then reacted with ozone to form an excited state of nitrogen dioxide. Upon returning to ground state, light energy is emitted. Then, TDN is measured using a chemiluminescence detector. Ammonia was determined using a Lachat's QuikChem 8500 Series 2 Flow Injection Analysis System (LACHAT Instruments, QuckChem 8500 series 2, Automated Ion Analyzer, Loveland, Colorado). When ammonia in water samples is heated (60 degrees C) with salicylate and hypochlorite in an alkaline phosphate buffer, an emerald green color is produced which is proportional to the ammonia concentration. The color is intensified by the addition of nitroprusside. Ethylenediaminetetraacetic acid (EDTA) is added to the buffer to prevent the interference of metal ions (Ca, Mg, and Fe etc.). Ammonia-N is then determined by LACHAT flow injection and a colorimetric assay at an absorbance wavelength 660 nm. (Reference: LACHAT Instruments: QuickChem Method 90-107-06-3-A, Determination of Ammonia by Flow Injection Analysis (High Throughput, Salicylate Method/DCIC) (Multi Matrix method). Written by Lynn Egan (Application group), February 08, 2011.) All files are labeled by location and variable, and data reported are the mean values upon replicate measurements. All samples were analyzed under a rigorous quality assurance and quality control (QA/QC) process as detailed in the methods. This data package contains (1) a zip file (tdn_ammonia_data_2015-2025.zip) containing a total of 299 files: 298 data files of ammonia and TDN data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (2) a file-level metadata (v7_20260901_flmd.csv) file that lists each file contained in the dataset with associated metadata; (3) a data dictionary (v7_20260901_dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type; (4) PDF and docx files for the determination of Method Detection Limits (MDLs) for TDN data, which has been updated in 2026-08; and (5) PDF and docx files for the detemination of Method Detection Limits (MDLs) for Ammonia and the Interferences by LACHAT Flow Injection Analysis. Missing values within the anion data files are noted as either "-9999" or "0.0" for not detectable (N.D.) data. There are a total of 105 locations containing TDN and Ammonia-N data. Update 2020-10-07: Updated the data files to remove times from the timestamps, so that only dates remain. The data values have not changed. Update 2021-04-11: Added Determination of Method Detection Limits (MDLs) for DIC, NPOC and TDN Analyses and Determination of Method Detection Limit for Ammonia and the Interferences by LACHAT Flow Injection Analysis documents, which can be accessed as PDFs or with Microsoft Word.Update on 6/10/2022: versioned updates to this dataset was made along with these changes: (1) updated total dissolved nitrogen and ammonia data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) restructure of units to comply with CSV reporting format requirements, (5) added -9999 for empty numerical cells, and (6) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv). Update on 2022-12-20: Updates were made to both the data files and reporting format specific files. Units were listed incorrectly, but have been fixed to reflect correct units (ug/L). File level metadata (flmd) and data dictionary (dd) files were updated to reflect the updated versions of these files. Available data was added up until 2022-06-01. Update on 2023-08-08: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-01-05. The file level metadata and data dictionary files were updated to reflect the additional data added. Update on 2024-03-11: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-10-27. Further, revisions to the data files were made to remove incorrect data points (from 1970 and 2001). The reporting format specific files were updated to reflect the additional data added. Revised versions of the PDF and docx files for determination of MDLs for TDN were added to replace previous versions. Update on 2025-05-15: Updates were made to both the data files and reporting format specific files. New available TDN and Ammonia-N data was added, up until the end of WY2024 (September 30, 2024). International Generic Sample Numbers (IGSNs), when registered, were added to the data files. The reporting format specific files were updated to reflect the additional data added. Update on 2026-09-01: Updates were made to both the data files and reporting format specific files. New available TDN and Ammonia-N data was added, up until the end of WY2025 (September 30, 2025). Updated versions, as of 2026-08-10, of the PDF and docx files for determination of MDLs for TDN data were added to this dataset.
54 ENVIRONMENTAL SCIENCES↗
Methodology to Define Delivery Accuracy Under Current Day ATC Operations
In order to enable arrival management concepts and solutions in a NextGen environment, ground- based sequencing and scheduling functions have been developed to support metering operations in the National Airspace System. These sequencing and scheduling algorithms as well as tools are designed to aid air traffic controllers in developing an overall arrival strategy. The ground systems being developed will support the management of aircraft to their Scheduled Times of Arrival (STAs) at flow-constrained meter points. This paper presents a methodology for determining the undelayed delivery accuracy for current day air traffic control operations. This new method analyzes the undelayed delivery accuracy at meter points in order to understand changes of desired flow rates as well as enabling definition of metrics that will allow near-future ground automation tools to successfully achieve desired separation at the meter points. This enables aircraft to meet their STAs while performing high precision arrivals. The research presents a possible implementation that would allow delivery performance of current tools to be estimated and delivery accuracy requirements for future tools to be defined, which allows analysis of Estimated Time of Arrival (ETA) accuracy for Time-Based Flow Management (TBFM) and the FAA's Traffic Management Advisor (TMA). TMA is a deployed system that generates scheduled time-of-arrival constraints for en- route air traffic controllers in the US. This new method of automated analysis provides a repeatable evaluation of the delay metrics for current day traffic, new releases of TMA, implementation of different tools, and across different airspace environments. This method utilizes a wide set of data from the Operational TMA-TBFM Repository (OTTR) system, which processes raw data collected by the FAA from operational TMA systems at all ARTCCs in the nation. The OTTR system generates daily reports concerning ATC status, intent and actions. Due to its availability, ease of use, and vast collection of data across several airspaces it was determined that the OTTR data set would be the best method to utilize moving forward with this analysis. The particular variables needed for further analysis were determined along with the necessary OTTR reports, by working closely with the repository team additional analysis reports were developed that provided key ETA and STA information at the freeze horizon. One major benefit of the OTTR data is that using the correct reports the data across several airports could be analyzed over large periods of time. The OTTR data processes the TBFM data daily and is stored in various formats across several airspaces. This allowed us to develop our own parsing methods and raw data processing that would not rely on other computationally expensive tools that perform more in depth analysis of similar sets of data. The majority of this work consisted of the development of the ability to filter flights to create a subset of flights that could be considered undelayed, which is defined as a flight at the freeze horizon with an ETA and STA difference that was minimal or close to zero. This was a broad method that allowed the consideration of a large data set which consisted of all the traffic across a two month period in 2013, the hottest and coldest months, arriving into four airports: George Bush Intercontinental, Denver International, Los Angeles International, and Phoenix Sky Harbor.
delivery accuracy↗
From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data
In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.
97 - MATHEMATICS AND COMPUTING↗
Unsteady Aerodynamic Testing Using the Dynamic Plunge Pitch and Roll Model Mount
A final report on the DyPPiR tests that were run are presented. Essentially it consists of two parts, a description of the data reduction techniques and the results. The data reduction techniques include three methods that were considered: 1) signal processing of wind on - wind off data; 2) using wind on data in conjunction with accelerometer measurements; and 3) using a dynamic model of the sting to predict the sting oscillations and determining the aerodynamic inputs using an optimization process. After trying all three, we ended up using method 1, mainly because of its simplicity and our confidence in its accuracy. The results section consists of time history plots of the input variables (angle of attack, roll angle, and/or plunge position) and the corresponding time histories of the output variables, C(sub L), C(sub D), C(sub m), C(sub l), C(sub m), C(sub n). Also included are some phase plots of one or more of the output variable vs. an input variable. Typically of interest are pitch moment coefficient vs. angle of attack for an oscillatory motion where the hysteresis loops can be observed. These plots are useful to determine the "more interesting" cases. Samples of the data as it appears on the disk are presented at the end of the report. The last maneuver, a rolling pull up, is indicative of the unique capabilities of the DyPPiR, allowing combinations of motions to be exercised at the same time.
Lutze, Frederick H.↗
Synoptic analyses, 5-, 2-, and 0.4-millibar surfaces for July 1974 through June 1976
Meteorological rocketsonde and satellite radiance data were employed for analyses of a continuing series of high altitude constant pressure charts. The methods of processing, the various types of data utilized and the analysis procedure are described. Broad-scale analyses of temperature and geopotential height for the Northern Hemisphere 5, 2, and 0.4 mb surfaces are presented for each week of the period July 1974 through June 1976. Brief discussions of the variations of the temperature and height fields throughout the two year period are also given.
Source record↗
Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context
One challenge that nuclear power plant system engineers are facing is continuous generation of an extremely large amount of equipment reliability (ER) data. These data elements come in textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) forms. They provide system engineers with valuable insights and information by discovering anomalous behaviors or degradation trends, identifying possible causes behind such behaviors and trends, and predicting their direct consequences. This paper directly targets the knowledge generation from ER data by putting “data into context.” We employ model-based system engineering (MBSE) of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by first identifying which of the developed MBSE elements they are referring to. This task is harder for textual data since the information contained in issue or maintenance reports needs to be “understood” by a computational tool. We called this process “knowledge extraction” since our methods extract knowledge from textual data. Last, once numeric and textual ER data elements have been processed and “understood,” we discover possible cause-effect relations among them. This is performed by observing whether a logical connection through the MBSE models exists, and if there is a temporal relationship among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.
97 - MATHEMATICS AND COMPUTING↗