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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Neural network approach to Space Shuttle Main Engine health monitoring

A neural network was trained to distinguish anomalies in Space Shuttle Main Engine sensor data from noisy normal steady-state sensor data. Power spectra of successive windows of individual sensor data were presented to a neural network using Kohonen's topological feature map training algorithm. The trained network for each sensor was then tested to determine if it would detect anomalies in the sensor data, and if so, the time at which the anomaly would be detected. Power spectra from a few hundred seconds of actual test data from NASA tests 901-364 and 904-044 were used to test the network. In both cases, the neural network detected the onset of anomalous engine behavior at approximately the same time within each test as the onset times reported by NASA and Rocketdyne experts in their post-test analyses.

Whitehead, Bruce A.↗
NASA NTRS2024

Wilson Corners, Solid Waste Management Unit 001(SWMU 01) 2023 Annual Long-Term Monitoring Report

This report presents a summary of the long-term monitoring (LTM) activities that occurred in 2023 at Wilson Corners, Solid Waste Management Unit 001, at Kennedy Space Center (KSC), Florida. Annual LTM of groundwater is being conducted at the site. Based on results from groundwater sampling activities performed during the 2019 through 2020 LTM reporting period and the 2020 and 2021 DPT groundwater sampling, it was determined that the LTM sampling plan was no longer meeting the goal of LTM because delineation was not verified and the installation of an air sparge (AS) system to treat the area of the High Concentration Plume was recommended. The AS System was installed in late 2022 and early 2023. System start-up activities were initiated in April 2023. Following system startup, several site wells required retrofitting to equip wellheads for withstanding the air pressure released from air sparge wells during system operation. Some site wells also required repair or abandonment, and replacement. Survey of location and top-of-casing of newly installed monitoring wells was combined with scheduled AS system survey activities and was completed in January 2024. The activities presented in this report include the February and April 2023 LTM monitoring well installations; March and April 2023 LTM and performance monitoring well water level gauging and sampling; November 2023 LTM well retrofits and repairs; a summary of December 2023 LTM well abandonments and installations (complete site well abandonment activities will be presented under a separate cover); and January 2024 LTM well survey. During the March and April 2023 sampling events, the low-flow sampling method was used, and samples were analyzed for a select list of volatile organic compounds. In March 2023, groundwater flow for the site was generally to the west was generally consistent with historical observations at the site. The Low Concentration Plume (LCP) continues to extend both horizontally and vertically beyond the terminal depth of the current monitoring well network. Data, inclusive of the 2023 LTM and baseline performance monitoring sampling events, indicate that the LCP encompasses an estimated 19.5 acres, compared to the 2021 LCP footprint, inclusive of the 2020 and 2021 DPT sampling events of 20.7 acres. The vertical extent of VOCs was historically delineated by monitoring wells screened greater than 48 feet below land surface (bls). The results from the three vertical extent monitoring wells screened below 48 feet bls that were sampled during the 2023 LTM indicate that groundwater vinyl chloride concentrations in these three wells are greater than the GCTL. As presented in the 2021 Long-Term Monitoring Report (NASA 2022), the KSCRT agreed to delay deeper investigations in this area to prevent the creation of additional pathways for vertical migration. Based on groundwater sampling activities performed in 2023, recommendations are to perform the next annual LTM sampling event, scheduled for April 2024 and to conduct quarterly performance monitoring of the AS System. The current selection of monitoring wells in the recommended 2024 LTM plan will provide an adequate data set for monitoring groundwater plume behavior; however, the LTM monitoring well network will be evaluated and refined based on 2024 LTM and year one performance monitoring data.

King Linnea↗
NASA NTRS1991

A neural network architecture for implementation of expert systems for real time monitoring

Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

Ramamoorthy, P. A.↗
NASA NTRS1987

An overview of the Mark 4A monitor and control system

The Deep Space Network (DSN) Monitor and Control System was completely changed during the conversion from Mark 3 to the Mark 4A configuration. The configuration employs shared data processing equipment between several co-located antennas, and incorporates much greater centralization of operations functions. The configuration is described and its performance is compared to that of the Mark 3 era.

Leflang, J. G.↗
NASA NTRS1989

Automated monitor and control for deep space network subsystems

The problem of automating monitor and control loops for Deep Space Network (DSN) subsystems is considered and an overview of currently available automation techniques is given. The use of standard numerical models, knowledge-based systems, and neural networks is considered. It is argued that none of these techniques alone possess sufficient generality to deal with the demands imposed by the DSN environment. However, it is shown that schemes that integrate the better aspects of each approach and are referenced to a formal system model show considerable promise, although such an integrated technology is not yet available for implementation. Frequent reference is made to the receiver subsystem since this work was largely motivated by experience in developing an automated monitor and control loop for the advanced receiver.

Smyth, P.↗
NASA NTRS1992

NCC Simulation Model: Simulating the operations of the network control center, phase 2

The simulation of the network control center (NCC) is in the second phase of development. This phase seeks to further develop the work performed in phase one. Phase one concentrated on the computer systems and interconnecting network. The focus of phase two will be the implementation of the network message dialogues and the resources controlled by the NCC. These resources are requested, initiated, monitored and analyzed via network messages. In the NCC network messages are presented in the form of packets that are routed across the network. These packets are generated, encoded, decoded and processed by the network host processors that generate and service the message traffic on the network that connects these hosts. As a result, the message traffic is used to characterize the work done by the NCC and the connected network. Phase one of the model development represented the NCC as a network of bi-directional single server queues and message generating sources. The generators represented the external segment processors. The served based queues represented the host processors. The NCC model consists of the internal and external processors which generate message traffic on the network that links these hosts. To fully realize the objective of phase two it is necessary to identify and model the processes in each internal processor. These processes live in the operating system of the internal host computers and handle tasks such as high speed message exchanging, ISN and NFE interface, event monitoring, network monitoring, and message logging. Inter process communication is achieved through the operating system facilities. The overall performance of the host is determined by its ability to service messages generated by both internal and external processors.

Benjamin, Norman M.↗
NASA NTRS2003

Time Analyzer for Time Synchronization and Monitor of the Deep Space Network

A software package has been developed to measure, monitor, and archive the performance of timing signals distributed in the NASA Deep Space Network. Timing signals are generated from a central master clock and distributed to over 100 users at distances up to 30 kilometers. The time offset due to internal distribution delays and time jitter with respect to the central master clock are critical for successful spacecraft navigation, radio science, and very long baseline interferometry (VLBI) applications. The instrument controller and operator interface software is written in LabView and runs on the Linux operating system. The software controls a commercial multiplexer to switch 120 separate timing signals to measure offset and jitter with a time-interval counter referenced to the master clock. The offset of each channel is displayed in histogram form, and "out of specification" alarms are sent to a central complex monitor and control system. At any time, the measurement cycle of 120 signals can be interrupted for diagnostic tests on an individual channel. The instrument also routinely monitors and archives the long-term stability of all frequency standards or any other 1-pps source compared against the master clock. All data is stored and made available for

Cole, Steven↗
NASA NTRS2022

Wilson Corners Solid Waste Management Unit (SWMU) 001: 2021 Annual Long-Term Monitoring Report, Kennedy Space Center, Florida

This report presents a summary of the long-term monitoring (LTM) activities that occurred in 2021 at Wilson Corners, Solid Waste Management Unit (SWMU) 001, at Kennedy Space Center (KSC), Florida. The site is monitored under KSC’s Resource Conservation and Recovery Act (RCRA) Corrective Action Program. Adaptive site management is being utilized through ongoing assessment, design, and interim measures (IM). Annual LTM of the groundwater is also being conducted at the site. This approach also meets the requirements of Chapter 62-780, Florida Administrative Code (F.A.C.). The goal of LTM at this site is threefold: to determine groundwater flow characteristics, monitor the downgradient concentration trends, and monitor select locations internal to the groundwater plume. Every 5 years, upgradient and side-gradient monitoring wells are sampled to verify delineation. The last time this was performed was in 2015. The sampling of these wells in 2020 was replaced with the direct push technology (DPT) investigations completed in October 2020 and April 2021. This DPT groundwater data was presented in an Advance Data Package (ADP) in September 2021 and discussed in the Implementation Work Plan (IWP) dated November 2021 for installation of an air sparge (AS) system. Based on results from groundwater sampling activities performed during the previous reporting period, including the 2020 and 2021 DPT groundwater sampling, it was determined that the LTM sampling plan was no longer meeting the goal of LTM because delineation was not verified. The 2021 LTM sampling plan was modified to include the sampling of monitoring wells located around the perimeter of the low concentration plume (LCP); the area with concentrations of contaminants of concern [COCs] greater than Groundwater Cleanup Target Levels [GCTLs]), and sampling of 10 monitoring wells proposed for installation (April 2021 KSC Remediation Team (KSCRT) Meeting, Decision 2104-D32). The modified LTM plan received team consensus at the September 2021 KSCRT Meeting (Decision Number 2109-D03), and sampling of the existing monitoring wells was completed in December 2021. The proposed monitoring wells are planned for installation in late 2022, concurrent with ongoing IM construction activities. December 2021 LTM data was presented at the May 2022 KSCRT Meeting, and activities are summarized in this report. The activities presented in this report include the December 2021 groundwater gauging of 42 monitoring wells and sampling of 44 monitoring wells. During the December 2021 event, the low flow sampling method was used, and samples were analyzed for a select list of volatile organic compounds (VOCs), including 1,1,2-trichloro-1,2,2-trifluoroethane (Freon 113). The following conclusions can be made based on the 2021 LTM results: - In December 2021, groundwater flow for the site was generally to the west at all intervals. This is generally consistent with historical observations at the site, with the exception of a southwest and southeast flow component observed at 34 to 48 feet below land surface (bls). - The vertical extent of VOCs was historically delineated by monitoring wells screened greater than 48 feet bls. The results from the two vertical extent monitoring wells, WILC-MW0078 (screened 65 to 70 feet bls) and WILC-MW0130 (screened 56 to 66 feet bls) that were sampled during the 2021 LTM indicate that groundwater vinyl chloride (VC) concentrations in both wells were greater than the GCTL. The Remediation Team has previously agreed to delay deeper DPT investigations in this area to prevent the creation of additional pathways for vertical migration. - The LCP continues to extend both horizontally, predominantly to the west, and vertically beyond the current monitoring well network, with some retraction observed to the southeast. Evaluation of this data combined with data from the 2020 and 2021 DPT sampling event indicate that the LCP encompasses an estimated 20.7 acres based on an expanded sampling area, as compared to the 2020 LCP footprint of 17.0 acres. - Freon 113 was not detected above GCTLs during the 2021 LTM event. Based on groundwater sampling activities performed in 2021, including April 2021 DPT groundwater sampling, the following recommendations are provided: - Perform the next LTM sampling event, targeted to occur in 2023, concurrently with the IM baseline sampling prior to AS system installation; - Include sampling from nine monitoring wells that are planned to be installed in late 2022, concurrent with upcoming IM construction activities. Installation of one deep vertical well, screened 70 to 80 feet bls, will be delayed to prevent the creation of an additional pathway for vertical migration; - Continue to sample under the modified annual LTM plan as presented in Table 4-1 concurrently with IM baseline sampling; and - Once the AS system install and start-up is complete, select monitoring wells from the LTM program will transition into the performance monitoring plan, and LTM will be temporarily discontinued. Performance monitoring will be performed quarterly, and the monitoring well network will be evaluated following the first performance monitoring sampling event.

long-term monitoring (LTM)↗
NASA NTRS1990

Ubiquitous condition monitoring - Key to networked-subsystem space propulsion systems

Various highlights of NASA's recently held space transportation propulsion and avionics symposia and the participation in this symposia by industry, the university community, and government organizations are discussed. The potential contributions of the integrated control and health monitoring (ICHM) field are considered. The goals of advanced ICHM technologies are reliability, operability, performance, and affordability. It is proposed that conventional liquid rocket engine configurations, characterized as noninteracting, stand-alone, compactly packaged and usually gimballed sets of hardware, can profit through embracing advanced ICHM capabilities.

Escher, William J. D.↗
NASA NTRS2010

Web-Based Interface for Command and Control of Network Sensors

This software allows for the visualization and control of a network of sensors through a Web browser interface. It is currently being deployed for a network of sensors monitoring Mt. Saint Helen s volcano; however, this innovation is generic enough that it can be deployed for any type of sensor Web. From this interface, the user is able to fully control and monitor the sensor Web. This includes, but is not limited to, sending "test" commands to individual sensors in the network, monitoring for real-world events, and reacting to those events

Wallick, Michael N.↗
NASA NTRS1994

Using the Global GPS Network and Other Satellite Data to Monitor Ionospheric Total Electron Content

A globally distributed network of dual-frequency global positioning system (GPS) receivers is the primary source of data used to measure ionospheric total electron content (TEC) on global scales. Maps of TEC useful for calibrating propagation delays, or monitoring the solar-terrestrial environment, can be produced using this continuously operating network. The maps can also form the basis of a TEC calibration service for users around the world. Potential users may include single-frequency satellite altimetry missions, satellite tracking stations, and astronomical observatories.

satellite altimetry↗
NASA NTRS1991

Neural network based expert system for compressor stall monitoring

This research is designed to apply a new information processing technology, artificial neural networks, to monitoring compressor stall. The outputs of neural networks support the dynamic knowledge data base of an expert system. This is the open-loop mode to avoid compressor stall. The integration of a control system with neural networks is the closed-loop mode in stall avoidance. The feasibility of the concept has been demonstrated for the compressor of 16-foot transonic/supersonic propulsion wind tunnels. The construction of a prototpye expert system has been initiated.

Lo, Ching F.↗
NASA NTRS1985

Characteristics of cosmic ray pole-equator anisotropy derived from spherical harmonic analysis of neutron monitor data

The spherical harmonic analysis of cosmic ray neutron data from the worldwide network neutron monitor stations during the years, 1966 to 1969 was carried out. The second zonal harmonic component obtained from the analysis corresponds to the Pole-Equator anisotropy of the cosmic ray neutron intensity. Such an anisotropy makes a semiannual variation. In addition to this, it is shown that the Pole-Equator anisotropy makes a variation depending on the interplanetary magnetic field (IMF) sector polarities around the passages of the IMF sector boundary. A mechanism to interpret these results is also discussed.

Takahashi, H.↗
NASA NTRS1982

NASA Deep Space Network operating control

The primary function of the Deep Space Network (DSN) is to provide effective and reliable tracking and data acquisition for planetary and interplanetary space flight missions. This involves providing data to flight project mission operations, accepting commands from mission operations and transmitting the commands to stations and spacecraft, and providing a record of telemetry and command data to mission operations. Also included are network performance monitoring, the generation of predictions for antenna pointing and signal acquisition, network scheduling, and network validation tests. Descriptions are given of the three facilities and six systems of the DSN. Also described are interfaces, automation and standardized procedures, and discrepancy reporting. It is pointed out that the greatest challenge facing the DSN is the implementation of NASA's Network Consolidation Program, which is scheduled to be completed in 1986. The objectives of this program are enumerated.

Weisman, W. D.↗
NASA NTRS2021

Wilson Corners SWMU 001 2019-2020 Annual Long-Term Monitoring Report Kennedy Space Center, Florida

This report presents a summary of the long-term monitoring (LTM) activities that occurred in 2019 and 2020 at Wilson Corners, Solid Waste Management Unit 001, at the John F. Kennedy Space Center (KSC), Florida. The site is monitored under KSC’s Resource Conservation and Recovery Act Corrective Action Program. Adaptive site management is being utilized through ongoing assessment, design, and interim measures. This approach also meets the requirements of Chapter 62-780, Florida Administrative Code. The goal of LTM at this site is threefold: to determine groundwater flow characteristics, monitor the downgradient concentration trends, and monitor select locations internal to the groundwater plume. Every 5 years, upgradient and side-gradient monitoring wells are sampled to verify delineation. The last time this was performed was in 2015. Sampling of these wells in 2020 was replaced with the direct push technology investigations in October 2020 and April 2021. The activities presented in this report include three field events: (1) December 2019 -groundwater gauging of 38 monitoring wells and sampling of 36 monitoring wells; (2) May 2020 - groundwater sampling of 7 monitoring wells; and (3) December 2020 - groundwater gauging of 49 monitoring wells and sampling of 43 monitoring wells. During the December 2019 and May 2020 events, monitoring wells were sampled using passive diffusion bags and were analyzed for a “full list” of volatile organic compounds (VOCs), including Freon 113. During the December 2020 event, the low flow sampling method was used, and samples were analyzed for a “select list” of VOCs, including Freon 113. The following conclusions can be made based on the 2019 and 2020 LTM results: •In December 2019 and December 2020, groundwater flow for the site was generally to the west with the individual zones only varying by occasional northerly and southerly components. This is generally consistent with historical observations at the site. •The GCTL plume continues to extend both horizontally and vertically beyond our current monitoring well network. •The vertical extent of VOCs was historically generally delineated by monitoring wells screened greater than 48 feet bls. The results from the two vertical extent monitoring wells WILC-MW0078 and WILC-MW0130 that were sampled during the 2019 and 2020 LTM indicate that groundwater VOC concentrations in both wells were greater than the GCTL. •Freon 113 was not detected above GCTLs during the 2019 and 2020 LTM events. Based on groundwater sampling activities performed during this reporting period, including recent DPT groundwater sampling conducted for the implementation of an air sparge system, the following recommendations are provided: •Install 10 new monitoring wells, which will be sampled along with 49 existing monitoring wells, to assist with delineation of the low concentration plume. The wells are listed inTable 4-1, and the proposed locations are presented on Figures4 -1 through 4-5. (Figures 4-1, 4-2, 4-3, and 4-5 also show updated plumes and proposed performance monitoring well locations. This information will be discussed and presented under a separate cover.) •Modify the annual LTM sampling plan as presented inTable 4-1. •Sampling for future events at the site will be conducted using low flow pumping methods. The next annual LTM sampling event is currently planned for December 2021. Please note the 10 new monitoring wells will not be installed before this event but will be installed as part of the upcoming IM construction and will be sampled during baseline sampling, along with the 49 existing monitoring wells. Following this sampling event, it is recommended that the Annual LTM be combined with the performance monitoring under the IM implementation, and LTM be temporarily discontinued. Objectives of the 2021 LTM are to: (i) evaluate groundwater gradient and flow direction by collecting depth to water measurements from LTM wells; (ii) continue monitoring the peripheral VOC trends in the northern, southern, and western portions of the site by monitoring existing wells and installing and monitoring new wells as recommended; and (iii) monitor select internal plume wells.

Jennifer Lynn Joyal↗