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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Automated pH Control of Nutrient Solution in a Hydroponic Plant Growth System

Over, the years, NASA has played an important role in providing to and the development of automated nutrient delivery and monitoring, systems for growing crops hydroponically for long term space missions. One example are the systems used in the Biomass Production Chamber (BPC) at Kennedy Space Center (KSC). The current KSC monitoring system is based on an engineering workstation using standard analog/digital input/output hardware and custom written software. The monitoring system uses completely separate sensors to provide a check of control sensor accuracy and has the ability to graphically display and store data form past experiment so that they are available for data analysis [Fortson, 1992]. In many cases, growing systems have not been fitted with the kind of automated control systems as used at KSC. The Center for Food and Environmental Systems for Human Exploration of Space (CFESH) located on the campus of Tuskegee University, has effectively grown sweetpotatoes and peanuts hydroponically for the past five years. However they have adjusted the pH electrical conductivity and volume of the hydroponic nutrient solution only manually at times when the solution was to be replenished or changed out according to its protocol (e.g. one-week, two-week, or two-day cycle). But the pH of the nutrient solution flowing through the channel is neither known nor controlled between the update, change out, or replenishment period. Thus, the pH of the nutrient solution is not held at an optimum level over the span of the plant's growth cycle. To solve this dilemma, an automated system for the control and data logging of pH data relative to sweetpotato production using the nutrient film technique (NFT) has been developed, This paper discusses a microprocessor-based system, which was designed to monitor, control, and record the pH of a nutrient solution used for growing sweetpotatoes using NFT.

Smith, B.↗
NASA NTRS1991

YUCSA: A CLIPS expert database system to monitor academic performance

The York University CLIPS Student Administrator (YUCSA), an expert database system implemented in C Language Integrated Processing System (CLIPS), for monitoring the academic performance of undergraduate students at York University, is discussed. The expert system component in the system has already been implemented for two major departments, and it is under testing and enhancement for more departments. Also, more elaborate user interfaces are under development. We describe the design and implementation of the system, problems encountered, and immediate future plans. The system has excellent maintainability and it is very efficient, taking less than one minute to complete an assessment of one student.

Toptsis, Anestis A.↗
NASA NTRSDate not supplied

Systems Health Monitoring: Integrating FMEA into Bayesian Networks

The foreseeable high traffic density suggests that a large number of electric propulsion systems will enter the airspace, and that they will also operate at high frequency, e.g., large number of take offs and landings per unit time. The reliability of such critical systems is therefore key to ensure high safety standards in the low-altitude airspace. Diagnostic systems, which aim at identifying incipient faults, can mitigate unexpected failures or lower-than-expected reliability by performing early fault detection by monitoring the systems. A key element of fault diagnosis is fault detection and isolation (FDI), which complexity increases with the complexity of the system itself, namely the number of subsystems and components, interactions among sub-systems, and the number of sensors available. The proposed approach leverages combination of failure mode and effect analysis (FMEA) integrated with Bayesian networks, thus introducing dependability structures into a diagnostic framework to aid FDI. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. The integrated framework enables the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. In this work, sub systems of Urban Air Mobility (UAM) type vehicle like avionics, structures, power-train etc. are taken into account to show the approach at the system level. This work integrates early design phase in the development of UAM type vehicles with diagnostic tools, which are often developed later in the product life-cycle, or retrofitted at a later time on systems. Failure mode and effect analysis (FMEA) derived for the system in the design phase is embedded within a Bayesian network (BN).

UAM↗
NASA NTRS1992

A global change data base using Thematic Mapper data - Earth Monitoring Educational System (EMES)

Some of the main directions in creating an education program in earth system science aimed at combining top science and technology with high academic performance are presented. The creation of an Earth Monitoring Educational System (EMES) integrated with the research interests of the NASA Ames Research Center and one or more universities is proposed. Based on the integration of a global network of cooperators to build a global data base for assessments of global change, EMES would promote degrees at all levels in global ecology at associated universities and colleges, and extracurricular courses for multilevel audiences. EMES objectives are to: train specialists; establish a tradition of solving regional problems concerning global change in a systemic manner, using remote sensing technology as the monitoring tool; and transfer knowledge on global change to the national and world communities. South America is proposed as the pilot continent for the project.

D'Antoni, Hector L.↗
NASA NTRS2002

A System for Monitoring and Management of Computational Grids

As organizations begin to deploy large computational grids, it has become apparent that systems for observation and control of the resources, services, and applications that make up such grids are needed. Administrators must observe the operation of resources and services to ensure that they are operating correctly and they must control the resources and services to ensure that their operation meets the needs of users. Users are also interested in the operation of resources and services so that they can choose the most appropriate ones to use. In this paper we describe a prototype system to monitor and manage computational grids and describe the general software framework for control and observation in distributed environments that it is based on.

Smith, Warren↗
NASA NTRS2013

Integrating Condition Indicators and Usage Parameters for Improved Spiral Bevel Gear Health Monitoring

The objective of this study was to illustrate the importance of combining Health Usage Monitoring Systems (HUMS) data with usage monitoring system data when detecting rotorcraft transmission health. Six gear sets were tested in the NASA Glenn Spiral Bevel Gear Fatigue Rig. Damage was initiated and progressed on the gear and pinion teeth. Damage progression was measured by debris generation and documented with inspection photos at varying torque values. A contact fatigue analysis was applied to the gear design indicating the effect temperature, load and reliability had on gear life. Results of this study illustrated the benefits of combining HUMS data and actual usage data to indicate progression of damage for spiral bevel gears.

Dempsey, Paula J.↗
NASA NTRS2013

Integrating Condition Indicators and Usage Parameters for Improved Spiral Bevel Gear Health Monitoring

The objective of this study was to illustrate the importance of combining Health Usage Monitoring Systems (HUMS) data with usage monitoring system data when detecting rotorcraft transmission health. Three gear sets were tested in the NASA Glenn Spiral Bevel Gear Fatigue Rig. Damage was initiated and progressed on the gear and pinion teeth. Damage progression was measured by debris generation and documented with inspection photos at varying torque values. A contact fatigue analysis was applied to the gear design indicating the effect temperature, load and reliability had on gear life. Results of this study illustrated the benefits of combining HUMS data and actual usage data to indicate progression of damage for spiral bevel gears.

Dempsey, Paula J.↗
NASA NTRS2003

Automated Monitoring with a BCP Fault-Decision Test

The Bayesian conditional probability (BCP) technique is a statistical fault-decision technique that is suitable as the mathematical basis of the fault-manager module in the automated-monitoring system and method described in the immediately preceding article. Within the automated-monitoring system, the fault-manager module operates in conjunction with the fault-detector module, which can be based on any one of several fault-detection techniques; examples include a threshold-limit-comparison technique or the BSP or SPRT technique mentioned in the preceding article. The present BCP technique is used to evaluate a series of one or more fault-detection events for the purpose of filtering out occasional false alarms produced by many types of statistical fault-detection procedures. The BCP technique increases the probability that an automated monitoring system produces a correct decision regarding the presence or absence of a fault. Because occasional false alarms are an inevitable consequence of the SPRT, BSP, or any other statistically based fault-detection test, there is a need for a logical procedure to distinguish between true and false alarms. Heretofore, it has been common practice to make a fault decision on an ad hoc basis for example by following a multiple-observation voting strategy in which a signal is declared to be indicative of a fault if m of the last n observations produced a fault-detection alarm. The BCP technique was developed to obtain results more reliable than those afforded by a voting strategy. The BCP technique involves a test in which one applies Bayesian inference techniques to a series of one or more single-observation alarms produced by a fault-detection test. One considers the last n decisions generated by a fault-detection test in order to evaluate the conditional probability that a failure is indicated (see figure). Each new decision reached by a fault-detection test is treated as a new piece of evidence about the state of the monitored asset, and the conditional probability of failure for the system is updated on the basis of this new evidence. The conditional probability of failure is compared with a predefined limit. For a probability below the limit, the asset is declared to be healthy. For a probability above the limit, the asset is declared to be faulty.

Bickford, Randall L.↗
NASA NTRS2007

Monitoring by Use of Clusters of Sensor-Data Vectors

The inductive monitoring system (IMS) is a system of computer hardware and software for automated monitoring of the performance, operational condition, physical integrity, and other aspects of the health of a complex engineering system (e.g., an industrial process line or a spacecraft). The input to the IMS consists of streams of digitized readings from sensors in the monitored system. The IMS determines the type and amount of any deviation of the monitored system from a nominal or normal ( healthy ) condition on the basis of a comparison between (1) vectors constructed from the incoming sensor data and (2) corresponding vectors in a database of nominal or normal behavior. The term inductive reflects the use of a process reminiscent of traditional mathematical induction to learn about normal operation and build the nominal-condition database. The IMS offers two major advantages over prior computational monitoring systems: The computational burden of the IMS is significantly smaller, and there is no need for abnormal-condition sensor data for training the IMS to recognize abnormal conditions. The figure schematically depicts the relationships among the computational processes effected by the IMS. Training sensor data are gathered during normal operation of the monitored system, detailed computational simulation of operation of the monitored system, or both. The training data are formed into vectors that are used to generate the database. The vectors in the database are clustered into regions that represent normal or nominal operation. Once the database has been generated, the IMS compares the vectors of incoming sensor data with vectors representative of the clusters. The monitored system is deemed to be operating normally or abnormally, depending on whether the vector of incoming sensor data is or is not, respectively, sufficiently close to one of the clusters. For this purpose, a distance between two vectors is calculated by a suitable metric (e.g., Euclidean distance) and "sufficiently close" signifies lying at a distance less than a specified threshold value. It must be emphasized that although the IMS is intended to detect off-nominal or abnormal performance or health, it is not necessarily capable of performing a thorough or detailed diagnosis. Limited diagnostic information may be available under some circumstances. For example, the distance of a vector of incoming sensor data from the nearest cluster could serve as an indication of the severity of a malfunction. The identity of the nearest cluster may be a clue as to the identity of the malfunctioning component or subsystem. It is possible to decrease the IMS computation time by use of a combination of cluster-indexing and -retrieval methods. For example, in one method, the distances between each cluster and two or more reference vectors can be used for the purpose of indexing and retrieval. The clusters are sorted into a list according to these distance values, typically in ascending order of distance. When a set of input data arrives and is to be tested, the data are first arranged as an ordered set (that is, a vector). The distances from the input vector to the reference points are computed. The search of clusters from the list can then be limited to those clusters lying within a certain distance range from the input vector; the computation time is reduced by not searching the clusters at a greater distance.

Iverson, David L.↗
NASA NTRS2011

Intelligent Wireless Sensor Networks for System Health Monitoring

Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network (PAN) standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. WSNs provide the inherent fault tolerance required for aerospace applications. The Discovery and Systems Health Group at NASA Ames Research Center has been developing WSN technology for use aboard aircraft and spacecraft for System Health Monitoring of structures and life support systems using funding from the NASA Engineering and Safety Center and Exploration Technology Development and Demonstration Program. This technology provides key advantages for low-power, low-cost ancillary sensing systems particularly across pressure interfaces and in areas where it is difficult to run wires. Intelligence for sensor networks could be defined as the capability of forming dynamic sensor networks, allowing high-level application software to identify and address any sensor that joined the network without the use of any centralized database defining the sensors characteristics. The IEEE 1451 Standard defines methods for the management of intelligent sensor systems and the IEEE 1451.4 section defines Transducer Electronic Datasheets (TEDS), which contain key information regarding the sensor characteristics such as name, description, serial number, calibration information and user information such as location within a vehicle. By locating the TEDS information on the wireless sensor itself and enabling access to this information base from the application software, the application can identify the sensor unambiguously and interpret and present the sensor data stream without reference to any other information. The application software is able to read the status of each sensor module, responding in real-time to changes of PAN configuration, providing the appropriate response for maintaining overall sensor system function, even when sensor modules fail or the WSN is reconfigured. The session will present the architecture and technical feasibility of creating fault-tolerant WSNs for aerospace applications based on our application of the technology to a Structural Health Monitoring testbed. The interim results of WSN development and testing including our software architecture for intelligent sensor management will be discussed in the context of the specific tradeoffs required for effective use. Initial certification measurement techniques and test results gauging WSN susceptibility to Radio Frequency interference are introduced as key challenges for technology adoption. A candidate Developmental and Flight Instrumentation implementation using intelligent sensor networks for wind tunnel and flight tests is developed as a guide to understanding key aspects of the aerospace vehicle design, test and operations life cycle.

networks↗
NASA NTRS1973

Evaluation of submarine strain-gage systems for monitoring coastal sediment migration

Single and multiple strain-gage systems were respectively evaluated as in situ point and areal sensors for monitoring sand-height variations in coastal environments. Static loading tests indicate that gage response pressure is linear for sand heights up to 24 inches. Response pressures are a function of both sand height and aggregate density, with density being influenced by both sediment texture and degree of compaction. Poorer sediment sorting and greater compaction result in higher response pressures. Field tests in a beach foreshore environment indicate that the gage systems are effective qualitative instruments for monitoring long-period migration trends of beach sediments; whereas, short-period responses are not sufficiently reliable. The durability and compactness of the gage systems must be substantially increased for effective field operations. It is recommended that the systems' qualitative potentials be further developed, whereas their development as quantitative instruments be terminated. Further development should emphasize the construction of remote recording systems designed for semipermanent installation.

Shideler, G. L.↗
NASA NTRS2024

Hypergol Maintenance Facility North, SWMU 090, Year 3 Air Sparge System Performance Monitoring Report, Kennedy Space Center, Florida

This Performance Monitoring Report (PMR) presents Year 3 Air Sparge (AS) System operation, maintenance, and monitoring (OM&M) activities and performance monitoring results for the AS Interim Measure (IM) at the Hypergol Maintenance Facility North (HMFN) at Kennedy Space Center (KSC), Florida. HMFN has been designated Solid Waste Management Unit (SWMU) 090 under the KSC Resource Conservation and Recovery Act Corrective Action Program. The timeframe for activities included in this report extends from September 2022 to August 2023. The HMFN AS IM was implemented in 2019-2020 to treat a chlorinated solvent groundwater plume that resulted from historical operations supporting the National Aeronautics and Space Administration (NASA) Space Program. The system includes 213 AS wells, with screen depths ranging from 25 feet to 45 feet below land surface (bls) and treats approximately 1.62 acres of contaminated groundwater. The objective of the AS IM is to actively reduce groundwater concentrations exceeding Florida Department of Environmental Protection Natural Attenuation Default Concentrations (NADCs) (identified as the High Concentration Plume [HCP]) to levels that facilitate transition into a Long-Term Monitoring program. The AS IM targets all of the HCP, except for a source zone area within the HCP where trichloroethene (TCE) concentrations exceed 11,000 micrograms per liter (μg/L) at depths greater than 45 feet bls. This is because site lithology was found to not be conducive to the AS treatment technology at these deeper depths. NASA will re-visit plans for potentially utilizing another remedial technology for residual deeper contamination following completion of the AS IM. OM&M activities and results from Year 3 indicate that the AS system at HMFN is operating as designed and is meeting performance criteria. Groundwater performance monitoring results indicate that following the third year of AS system operation, TCE concentrations in wells across all treated depth intervals have been reduced by an average of more than 99 percent. Overall, the areal extent of the plume showed a reduction following Year 3 of AS operations; however, continued operation of the AS system is required to meet the IM objective.

VOCs↗
NASA NTRS2000

Propulsion IVHM Extreme Environment Instrumentation Power IVHM

This paper presents propulsion and instrumentation power for integrated vehicle health management technologies. The topics include: 1) Propulsion IVHM Capabilities Research; 2) Projects: X-33 Post-Test Diagnostic System; 3) X-34 NITEX; 4) Advanced Health Monitoring Systems; 5) Active Vibration Monitoring System; 6) Smart Self Healing Propulsion Systems; 7) Extreme Environment Sensors; and 8) Systems Engineering and Integration.

Zakrajsek, June↗
NASA NTRS2007

Solutions Network Formulation Report. The Potential Contribution of the International GPM Program to the NOAA Estuarine Reserves Division's System-wide Monitoring Program

Data collected via the International GPM Program could be used to provide a solution for the NOAA Estuarine Reserves Division s System-wide Monitoring Program by augmenting in situ rainfall measurements with data acquired via future satellite-acquired precipitation data. This Candidate Solution is in alignment with the Coastal Management National Application and will benefit society by assisting in estuary preservation.

Hilbert, Kent↗
NASA NTRS1977

Feasibility of a fetal measurement electrode system

Findings of the study are summarized and conclude that all monitoring requirements are not currently satisfied. An approach is presented to provide a multiparametric monitoring system through combinations of existing transducers. This monitoring system would be appropriate, not only for intrapartum monitoring, but also for neonatal and adult blood gas evaluations. A literature search was conducted to provide an insight into current state-of-the-art in fetal monitoring.

Source record↗
NASA NTRS1998

Making the Impossible Possible: Strategies for Fast POMDP Monitoring

Systems modeled as partially observable Markov decision processes (POMDPs) can be tracked quickly with three restrictions: all actions are grouped together, the out-degree of each system state is bounded by a constant, and the number of non-zero elements in the belief state is bounded by a (different) constant. With these restrictions, the tracking algorithm operates in constant time and linear space. The first restriction assumes that the action itself is unobservable. The second restriction defines a subclass of POMDPs that covers however a wide range of problems. The third restriction is an approximation technique that can lead to a potentially vexing problem: an observation may be received that has probability according to the restricted belief state. This problem of impossibility will cause the belief state to collapse. In this paper we discuss the tradeoffs between the constant bound on the belief state and the quality of the solution. We concentrate on strategies for overcoming the impossibility problem and demonstrate initial experimental results that indicate promising directions.

Washington, Richard↗