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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Discovering System Health Anomalies Using Data Mining Techniques

We present a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an Integrated System Health Monitoring system. We specifically treat the problem of discovering anomalous features in the time series that may be indicative of a system anomaly, or in the case of a manned system, an anomaly due to the human. Identification of these anomalies is crucial to building stable, reusable, and cost-efficient systems. The framework consists of an analysis platform and new algorithms that can scale to thousands of sensor streams to discovers temporal anomalies. We discuss the mathematical framework that underlies the system and also describe in detail how this framework is general enough to encompass both discrete and continuous sensor measurements. We also describe a new set of data mining algorithms based on kernel methods and hidden Markov models that allow for the rapid assimilation, analysis, and discovery of system anomalies. We then describe the performance of the system on a real-world problem in the aircraft domain where we analyze the cockpit data from aircraft as well as data from the aircraft propulsion, control, and guidance systems. These data are discrete and continuous sensor measurements and are dealt with seamlessly in order to discover anomalous flights. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.

Sriastava, Ashok, N.↗
NASA NTRS2014

Analysis of Airport Ground Delay Program Decisions Using Data Mining Techniques

Air traffic service providers have to make decisions regarding changes to air traffic flow in the event of major weather disturbances and traffic congestions to maintain safety of the system. The behavior of the air traffic management system will be more predictable if consistent decisions are made under similar traffic and weather conditions. Consistency of deciding on control action depends on the weather and traffic conditions as well as accuracy in predicting these conditions. Weather parameters (defined in terms of forecast and actual weather and traffic conditions) on different days can be used to categorize days into days with little decision consistency, days with moderate decision consistency and days with high decision consistency. Four years of traffic, weather and ground delay program decisions data at major airports in the United States are used in the analysis. This paper examines performance of different data mining methods in the three regions of decision consistency. Not surprisingly, data mining methods have the best performance in the region of most decision consistency and have the poorest performance in the region of little decision consistency. In applications where data mining methods have differing performance in differing regions, it would be more useful to characterize the region specific performance instead of characterizing performance by a single parameter. Finally, the results show no significant variation in the performance of different data mining methods for this particular problem. The fact that different mining methods show no significant variation also provides further confidence in the results of data mining methods. Work in this abstract discusses initial results. This paper describes the results in terms of both forecast and actual environmental conditions and discusses how prediction errors impact decision consistency.

Kulkarni, Deepak↗
NASA NTRSDate not supplied

Data Fusion and Mining Techniques to Map Water Use and Drought across Spatial and Temporal Scales

As the world’s water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use (e.g, ET) are becoming increasingly important, especially in areas of significant water and food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in mid-morning land surface temperature to estimate the partitioning of sensible and latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring. Until recently, ALEXI has been limited to areas with high resolution temporal sampling of geostationary sensors. The use of geostationary sensors makes global mapping a complicated process, especially for real-time applications, as data from as many as five different sensors are required to be ingested and harmonized to create a global mosaic. However, our research team has developed a new and novel method of using twice-daily observations from polar-orbiting sensors such as MODIS and VIIRS to estimate the mid-morning rise in LST that is used to drive the energy balance estimations within ALEXI. This allows the method to be applied globally using a single sensor (in this case, initially MODIS with a planned transition to VIIRS) rather than a global compositing of all available geostationary data. Other advantages of this new method include the higher spatial resolution provided by MODIS and VIIRS and the increased sampling at high latitudes where oblique view angles limit the utility of geostationary sensors. This presentation will focus on global applications for mapping water use and drought using data mining and data fusion across spatial scales extending from 5-km to 30-m “field-scale” estimates.

Christopher Hain↗
NASA NTRS2004

Virtual Sensors: Using Data Mining Techniques to Efficiently Estimate Remote Sensing Spectra

Various instruments are used to create images of the Earth and other objects in the universe in a diverse set of wavelength bands with the aim of understanding natural phenomena. These instruments are sometimes built in a phased approach, with some measurement capabilities being added in later phases. In other cases, there may not be a planned increase in measurement capability, but technology may mature to the point that it offers new measurement capabilities that were not available before. In still other cases, detailed spectral measurements may be too costly to perform on a large sample. Thus, lower resolution instruments with lower associated cost may be used to take the majority of measurements. Higher resolution instruments, with a higher associated cost may be used to take only a small fraction of the measurements in a given area. Many applied science questions that are relevant to the remote sensing community need to be addressed by analyzing enormous amounts of data that were generated from instruments with disparate measurement capability. This paper addresses this problem by demonstrating methods to produce high accuracy estimates of spectra with an associated measure of uncertainty from data that is perhaps nonlinearly correlated with the spectra. In particular, we demonstrate multi-layer perceptrons (MLPs), Support Vector Machines (SVMs) with Radial Basis Function (RBF) kernels, and SVMs with Mixture Density Mercer Kernels (MDMK). We call this type of an estimator a Virtual Sensor because it predicts, with a measure of uncertainty, unmeasured spectral phenomena.

Srivastava, Ashok N.↗
NASA NTRS1975

Assessment of practicality of remote sensing techniques for a study of the effects of strip mining in Alabama

Because of the volume of coal produced by strip mining, the proximity of mining operations, and the diversity of mining methods (e.g. contour stripping, area stripping, multiple seam stripping, and augering, as well as underground mining), the Warrior Coal Basin seemed best suited for initial studies on the physical impact of strip mining in Alabama. Two test sites, (Cordova and Searles) representative of the various strip mining techniques and environmental problems, were chosen for intensive studies of the correlation between remote sensing and ground truth data. Efforts were eventually concentrated in the Searles Area, since it is more accessible and offers a better opportunity for study of erosional and depositional processes than the Cordova Area.

Hughes, T. H.↗
DOE OSTI2020

How do the research and public communities view biofuel development?

Understanding how public and research view biofuel development, especially the unsynchronized views, can explain the gaps between the actual biofuel production and consumption and the government mandates in the US. Applying a comparative text mining technique, these views are explored using 9924 news articles and 57,849 research abstracts. It is found both the public and research communities respond actively to major policy and incentive programs and market conditions and the public has been closely following important research advances. However, research and practice should be coordinated to achieve economies of scale for regular and alternative biofuels. Priority is particularly needed for policy research to support legislation and enforcement and remove technical barriers to commercialization; meanwhile, greater attention is needed to prompt the commercialization of mature technologies. In addition, sentiment analysis shows positive public perceptions in general and negative perceptions primarily stemming from fraud in biofuel tax-credit programs with minor concerns on unintended negative impacts of biofuel development and policy implementation issues. This study contributes to methodology by using cutting-edge text-mining techniques to the largest and most up-to-date datasets of public news articles and research publication abstracts so as to compare public and academic views on biofuel development. As a result, this study also contributes to knowledge discovery by providing new perspectives on biofuel development, which will support policy design and identify future research directions.

09 BIOMASS FUELS↗
NASA NTRS2012

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery

Space Propulsion↗
NASA NTRS2009

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, satellite vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed, or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery.

Satellites↗
NASA NTRS1981

Application of LANDSAT data to monitor land reclamation progress in Belmont County, Ohio

Strip and contour mining techniques are reviewed as well as some studies conducted to determine the applicability of LANDSAT and associated digital image processing techniques to the surficial problems associated with mining operations. A nontraditional unsupervised classification approach to multispectral data is considered which renders increased classification separability in land cover analysis of surface mined areas. The approach also reduces the dimensionality of the data and requires only minimal analytical skills in digital data processing.

Bloemer, H. H. L.↗
NASA NTRS2006

Enabling the Discovery of Recurring Anomalies in Aerospace System Problem Reports using High-Dimensional Clustering Techniques

This paper describes the results of a significant research and development effort conducted at NASA Ames Research Center to develop new text mining techniques to discover anomalies in free-text reports regarding system health and safety of two aerospace systems. We discuss two problems of significant importance in the aviation industry. The first problem is that of automatic anomaly discovery about an aerospace system through the analysis of tens of thousands of free-text problem reports that are written about the system. The second problem that we address is that of automatic discovery of recurring anomalies, i.e., anomalies that may be described m different ways by different authors, at varying times and under varying conditions, but that are truly about the same part of the system. The intent of recurring anomaly identification is to determine project or system weakness or high-risk issues. The discovery of recurring anomalies is a key goal in building safe, reliable, and cost-effective aerospace systems. We address the anomaly discovery problem on thousands of free-text reports using two strategies: (1) as an unsupervised learning problem where an algorithm takes free-text reports as input and automatically groups them into different bins, where each bin corresponds to a different unknown anomaly category; and (2) as a supervised learning problem where the algorithm classifies the free-text reports into one of a number of known anomaly categories. We then discuss the application of these methods to the problem of discovering recurring anomalies. In fact the special nature of recurring anomalies (very small cluster sizes) requires incorporating new methods and measures to enhance the original approach for anomaly detection. ?& pant 0-

Srivastava, Ashok, N.↗
NASA NTRS1982

Comparing digital data processing techniques for surface mine and reclamation monitoring

The results of three techniques used for processing Landsat digital data are compared for their utility in delineating areas of surface mining and subsequent reclamation. An unsupervised clustering algorithm (ISOCLS), a maximum-likelihood classifier (CLASFY), and a hybrid approach utilizing canonical analysis (ISOCLS/KLTRANS/ISOCLS) were compared by means of a detailed accuracy assessment with aerial photography at NASA's Goddard Space Flight Center. Results show that the hybrid approach was superior to the traditional techniques in distinguishing strip mined and reclaimed areas.

Witt, R. G.↗
NASA NTRS1978

Utilization of lunar materials in space

Reasons for conducting commercial mining operations on the moon are discussed with attention to physical parameters, material abundances, and economics. Adaptations of currently used mining techniques are considered, and space applications of moon-derived materials are suggested. Possible organization of the mining project is examined, and it is suggested that the transition from concept phase to implementation could proceed rapidly. Characteristics of maturing space industries and the roles of the public and the private sectors are considered.

Criswell, D. R.↗
DOE OSTI2020

A data mining approach to investigate food groups related to incidence of bladder cancer in the BLadder cancer Epidemiology and Nutritional Determinants International Study

Abstract At present, analysis of diet and bladder cancer (BC) is mostly based on the intake of individual foods. The examination of food combinations provides a scope to deal with the complexity and unpredictability of the diet and aims to overcome the limitations of the study of nutrients and foods in isolation. This article aims to demonstrate the usability of supervised data mining methods to extract the food groups related to BC. In order to derive key food groups associated with BC risk, we applied the data mining technique C5.0 with 10-fold cross-validation in the BLadder cancer Epidemiology and Nutritional Determinants study, including data from eighteen case–control and one nested case–cohort study, compromising 8320 BC cases out of 31 551 participants. Dietary data, on the eleven main food groups of the Eurocode 2 Core classification codebook, and relevant non-diet data (i.e. sex, age and smoking status) were available. Primarily, five key food groups were extracted; in order of importance, beverages (non-milk); grains and grain products; vegetables and vegetable products; fats, oils and their products; meats and meat products were associated with BC risk. Since these food groups are corresponded with previously proposed BC-related dietary factors, data mining seems to be a promising technique in the field of nutritional epidemiology and deserves further examination.

Nutrition & Dietetics↗
NASA NTRS2005

Vibration Based Crack Detection in a Rotating Disk: Experimental Results - Part 2

This paper describes the experimental results concerning the detection of a crack in a rotating disk. The goal was to utilize blade tip clearance and shaft vibration measurements to monitor changes in the system's center of mass and/or blade deformation behaviors. The concept of the approach is based on the fact that the development of a disk crack results in a distorted strain field within the component. As a result, a minute deformation in the disk's geometry as well as a change in the system's center of mass occurs. Here, a notch was used to simulate an actual crack. The vibration based experimental results failed to identify the existence of a notch when utilizing the approach described above, even with a rather large, circumferential notch (l.2 in.) located approximately mid-span on the disk (disk radius = 4.63 in. with notch at r = 2.12 in.). This was somewhat expected, since the finite element based results in Part 1 of this study predicted changes in blade tip clearance as well as center of mass shifts due to a notch to be less than 0.001 in. Therefore, the small changes incurred by the notch could not be differentiated from the mechanical and electrical noise of the rotor system. Although the crack detection technique of interest failed to identify the existence ofthe notch, the vibration data produced and captured here will be utilized in upcoming studies that will focus on different data mining techniques concerning damage detection in a disk.

Gyekenyesi, Andrew L.↗
DOE OSTI2022

Process mining for healthcare: Characteristics and challenges

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

59 BASIC BIOLOGICAL SCIENCES↗