Results for “processes control processes” — Page 28

NASA NTRS2003

Regulatory gene networks and the properties of the developmental process

Genomic instructions for development are encoded in arrays of regulatory DNA. These specify large networks of interactions among genes producing transcription factors and signaling components. The architecture of such networks both explains and predicts developmental phenomenology. Although network analysis is yet in its early stages, some fundamental commonalities are already emerging. Two such are the use of multigenic feedback loops to ensure the progressivity of developmental regulatory states and the prevalence of repressive regulatory interactions in spatial control processes. Gene regulatory networks make it possible to explain the process of development in causal terms and eventually will enable the redesign of developmental regulatory circuitry to achieve different outcomes.

Non-NASA Center↗

NASA NTRS1997

Digital Images and Human Vision

Processing of digital images destined for visual consumption raises many interesting questions regarding human visual sensitivity. This talk will survey some of these questions, including some that have been answered and some that have not. There will be an emphasis upon visual masking, and a distinction will be drawn between masking due to contrast gain control processes, and due to processes such as hypothesis testing, pattern recognition, and visual search.

Watson, Andrew B.↗

NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, aviation incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts entities relevant to safety analysts. The custom NER model is built by fine-tuning an existing Bidirectional Encoder Representations from Transformers (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from failure-relevant text. This model performs passably, with a weighted average f1 score of 0.33 across entity types, indicating more labeled training data is needed. Extracted entities are used to form a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported using the SAFECOM system. Similar mishaps are manually clustered and reported as single rows within an FMEA. Foreach cluster, we compute frequency, severity, and overall riskin accordance with FAA standards. This methodology can beapplied as part of a broader safety management system totrack trends in mishaps (e.g., likelihood, severity) and discoverknowledge (e.g., causes, effects) that can be utilized to improvesafety outcomes and system performance.

Machine Learning↗

NASA NTRS1991

Automatic Control Of Length Of Welding Arc

Nonlinear relationships among current, voltage, and length stored in electronic memory. Conceptual microprocessor-based control subsystem maintains constant length of welding arc in gas/tungsten arc-welding system, even when welding current varied. Uses feedback of current and voltage from welding arc. Directs motor to set position of torch according to previously measured relationships among current, voltage, and length of arc. Signal paths marked "calibration" or "welding" used during those processes only. Other signal paths used during both processes. Control subsystem added to existing manual or automatic welding system equipped with automatic voltage control.

Iceland, William F.↗

NASA NTRS1992

TQM in a test environment

In response to the changing aerospace economic climate, Martin Marietta Astronautics Group (MMAG) has adopted a Total Quality Management (TQM) philosophy to maintain a competitive edge. TQM emphasizes continuous improvement of processes, motivation to improve from within, cross-functional involvement, people empowerment, customer satisfaction, and modern process control techniques. The four major initiatives of TQM are Product Excellence, Manufacturing Resource Planning (MRP II), People Empowerment, and Subcontract Management. The Defense Space and Communications (DS&C) Test Lab's definition and implementation of the MRP II and people empowerment initiatives within TQM are discussed. The application of MRP II to environmental test planning and operations processes required a new and innovative approach. In an 18 month span, the test labs implemented MRP II and people empowerment and achieved a Class 'A' operational status. This resulted in numerous benefits, both tangible and intangible, including significant cost savings and improved quality of life. A detailed description of the implementation process and results are addressed.

Chambers, Gary D.↗

NASA NTRS1980

Adaptive/learning control of large space structures - System identification techniques

Techniques developed for the control of aircraft under changing operating conditions are used to develop a learning control system structure for a multi-configuration, flexible space vehicle. A configuration identification subsystem that is to be used with a learning algorithm and a memory and control process subsystem is developed. Adaptive gain adjustments can be achieved by this learning approach without prestoring of large blocks of parameter data and without dither signal inputs which will be suppressed during operations for which they are not compatible. The Space Shuttle Solar Electric Propulsion (SEP) experiment is used as a sample problem for the testing of adaptive/learning control system algorithms.

Thau, F. E.↗

NASA NTRS2016

Check Calibration of the NASA Glenn 10- by 10-Foot Supersonic Wind Tunnel (2014 Test Entry)

A check calibration of the 10- by 10-Foot Supersonic Wind Tunnel (SWT) was conducted in May/June 2014 using an array of five supersonic wedge probes to verify the 1999 Calibration. This check calibration was necessary following a control systems upgrade and an integrated systems test (IST). This check calibration was required to verify the tunnel flow quality was unchanged by the control systems upgrade prior to the next test customer beginning their test entry. The previous check calibration of the tunnel occurred in 2007, prior to the Mars Science Laboratory test program. Secondary objectives of this test entry included the validation of the new Cobra data acquisition system (DAS) against the current Escort DAS and the creation of statistical process control (SPC) charts through the collection of series of repeated test points at certain predetermined tunnel parameters. The SPC charts secondary objective was not completed due to schedule constraints. It is hoped that this effort will be readdressed and completed in the near future.

pressure measurement↗

NASA NTRS2011

International Space Station Acoustics - A Status Report

It is important to control acoustic noise aboard the International Space Station (ISS) to provide a satisfactory environment for voice communications, crew productivity, and restful sleep, and to minimize the risk for temporary and permanent hearing loss. Acoustic monitoring is an important part of the noise control process on ISS, providing critical data for trend analysis, noise exposure analysis, validation of acoustic analysis and predictions, and to provide strong evidence for ensuring crew health and safety, thus allowing Flight Certification. To this purpose, sound level meter (SLM) measurements and acoustic noise dosimetry are routinely performed. And since the primary noise sources on ISS include the environmental control and life support system (fans and airflow) and active thermal control system (pumps and water flow), acoustic monitoring will indicate changes in hardware noise emissions that may indicate system degradation or performance issues. This paper provides the current acoustic levels in the ISS modules and sleep stations, and is an update to the status presented in 20031. Many new modules, and sleep stations have been added to the ISS since that time. In addition, noise mitigation efforts have reduced noise levels in some areas. As a result, the acoustic levels on the ISS have improved.

Allen, Christopher S.↗

NASA NTRS2011

Generic, Extensible, Configurable Push-Pull Framework for Large-Scale Science Missions

The push-pull framework was developed in hopes that an infrastructure would be created that could literally connect to any given remote site, and (given a set of restrictions) download files from that remote site based on those restrictions. The Cataloging and Archiving Service (CAS) has recently been re-architected and re-factored in its canonical services, including file management, workflow management, and resource management. Additionally, a generic CAS Crawling Framework was built based on motivation from Apache s open-source search engine project called Nutch. Nutch is an Apache effort to provide search engine services (akin to Google), including crawling, parsing, content analysis, and indexing. It has produced several stable software releases, and is currently used in production services at companies such as Yahoo, and at NASA's Planetary Data System. The CAS Crawling Framework supports many of the Nutch Crawler's generic services, including metadata extraction, crawling, and ingestion. However, one service that was not ported over from Nutch is a generic protocol layer service that allows the Nutch crawler to obtain content using protocol plug-ins that download content using implementations of remote protocols, such as HTTP, FTP, WinNT file system, HTTPS, etc. Such a generic protocol layer would greatly aid in the CAS Crawling Framework, as the layer would allow the framework to generically obtain content (i.e., data products) from remote sites using protocols such as FTP and others. Augmented with this capability, the Orbiting Carbon Observatory (OCO) and NPP (NPOESS Preparatory Project) Sounder PEATE (Product Evaluation and Analysis Tools Elements) would be provided with an infrastructure to support generic FTP-based pull access to remote data products, obviating the need for any specialized software outside of the context of their existing process control systems. This extensible configurable framework was created in Java, and allows the use of different underlying communication middleware (at present, both XMLRPC, and RMI). In addition, the framework is entirely suitable in a multi-mission environment and is supporting both NPP Sounder PEATE and the OCO Mission. Both systems involve tasks such as high-throughput job processing, terabyte-scale data management, and science computing facilities. NPP Sounder PEATE is already using the push-pull framework to accept hundreds of gigabytes of IASI (infrared atmospheric sounding interferometer) data, and is in preparation to accept CRIMS (Cross-track Infrared Microwave Sounding Suite) data. OCO will leverage the framework to download MODIS, CloudSat, and other ancillary data products for use in the high-performance Level 2 Science Algorithm. The National Cancer Institute is also evaluating the framework for use in sharing and disseminating cancer research data through its Early Detection Research Network (EDRN).

Foster, Brian M.↗

NASA NTRS2015

International Space Station Acoustics - A Status Report

It is important to control acoustic noise aboard the International Space Station (ISS) to provide a satisfactory environment for voice communications, crew productivity, alarm audibility, and restful sleep, and to minimize the risk for temporary and permanent hearing loss. Acoustic monitoring is an important part of the noise control process on ISS, providing critical data for trend analysis, noise exposure analysis, validation of acoustic analyses and predictions, and to provide strong evidence for ensuring crew health and safety, thus allowing Flight Certification. To this purpose, sound level meter (SLM) measurements and acoustic noise dosimetry are routinely performed. And since the primary noise sources on ISS include the environmental control and life support system (fans and airflow) and active thermal control system (pumps and water flow), acoustic monitoring will reveal changes in hardware noise emissions that may indicate system degradation or performance issues. This paper provides the current acoustic levels in the ISS modules and sleep stations and is an update to the status presented in 2011. Since this last status report, many payloads (science experiment hardware) have been added and a significant number of quiet ventilation fans have replaced noisier fans in the Russian Segment. Also, noise mitigation efforts are planned to reduce the noise levels of the T2 treadmill and levels in Node 3, in general. As a result, the acoustic levels on the ISS continue to improve.

Allen, Christopher S.↗

NASA NTRS2005

A Review of Diagnostic Techniques for ISHM Applications

System diagnosis is an integral part of any Integrated System Health Management application. Diagnostic applications make use of system information from the design phase, such as safety and mission assurance analysis, failure modes and effects analysis, hazards analysis, functional models, fault propagation models, and testability analysis. In modern process control and equipment monitoring systems, topological and analytic , models of the nominal system, derived from design documents, are also employed for fault isolation and identification. Depending on the complexity of the monitored signals from the physical system, diagnostic applications may involve straightforward trending and feature extraction techniques to retrieve the parameters of importance from the sensor streams. They also may involve very complex analysis routines, such as signal processing, learning or classification methods to derive the parameters of importance to diagnosis. The process that is used to diagnose anomalous conditions from monitored system signals varies widely across the different approaches to system diagnosis. Rule-based expert systems, case-based reasoning systems, model-based reasoning systems, learning systems, and probabilistic reasoning systems are examples of the many diverse approaches ta diagnostic reasoning. Many engineering disciplines have specific approaches to modeling, monitoring and diagnosing anomalous conditions. Therefore, there is no "one-size-fits-all" approach to building diagnostic and health monitoring capabilities for a system. For instance, the conventional approaches to diagnosing failures in rotorcraft applications are very different from those used in communications systems. Further, online and offline automated diagnostic applications are integrated into an operations framework with flight crews, flight controllers and maintenance teams. While the emphasis of this paper is automation of health management functions, striking the correct balance between automated and human-performed tasks is a vital concern.

Patterson-Hine, Ann↗

NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported in SAFECOM. The custom NER model is built by fine-tuning an existing (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from any failure-relevant text. Similar mishaps are clustered and reported as single rows within the FMEA. For each cluster, frequency, severity, and overall risk are computed. The methodology can be applied as part of a broader safety management system to track trends in mishaps and discover knowledge that can be utilized to improve safety outcomes and system performance.

Machine Learning↗

NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported in SAFECOM. The custom NER model is built by fine-tuning an existing (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from any failure-relevant text. Similar mishaps are clustered and reported as single rows within the FMEA. For each cluster, frequency, severity, and overall risk are computed. The methodology can be applied as part of a broader safety management system to track trends in mishaps and discover knowledge that can be utilized to improve safety outcomes and system performance.

Machine Learning↗

NASA NTRS1992

High pressure turbomachinery ground test facility

Turbomachinery test facilities are at present scarce to non-existent world-wide. The turbomachinery test facility at Stennis Space Center will provide for advanced development and research and development capabilities for liquid hydrogen/liquid oxygen propellant rocket engine components. The facility will provide ultra-high pressure via gas generators to deliver the needed turbine drive on various turbomachinery. State of the art process control systems will provide the vital pressure, temperature and flow requirements during tests. These systems will better control adverse transient conditions during start-up and shutdown, and by using advanced control theory, as well as incorporate test article health monitoring. Also, digital data acquisition systems will obtain high frequency (up to 20 KHz) and low frequency (up to 1 KHz) data during the test. Pressures of up to 15,000 psi will be generated to pressurize high pressure tanks supplying cryogens to various test article inlets thus pushing turbopump materials and manufacturing processes to their limits. By planning for future projects the test facility will be easily adaptable to multi-program test configurations over a range of thermodynamic positions.

Scheuermann, Patrick E.↗

NASA NTRSDate not supplied

Leveraging CSPP: Building a cloud based direct broadcast processing system

Reducing the time that it takes to have useful satellite information is very important because timely access allows for more informed decision making. This is especially true in time critical situations like disaster response and financial market analysis. One way to achieve reductions in the overall time between information capture and delivery to use the direct broadcast from weather satellites. In this work, we describe a state driven satellite information system that captures a satellite’s direct broadcast signal and uses cloud-based resources to provide end-user controlled processing. The system takes advantage of the reliability and customizability of Amazon Web Services to provide fast and reliable access to a system that takes the direct broadcast signal and leverages the CSPP software as well as dynamically supplied end-user processing modules to produce a user desired information product. Finally, we describe the development process and how a flexible design allowed for changes as the capabilities of the processing platform evolved and the lessons we learned from the process.

CSPP↗

NASA NTRS1991

Center for Space Power, Texas A and M University

Johnson Controls is a 106 year old company employing 42,000 people worldwide with $4.7 billion annual sales. Though we are new to the aerospace industry we are a world leader in automobile battery manufacturing, automotive seating, plastic bottling, and facilities environment controls. The battery division produces over 24,000,000 batteries annually under private label for the new car manufacturers and the replacement market. We are entering the aerospace market with the nickel hydrogen battery with the help of NASA's Center for Space Power at Texas A&M. Unlike traditional nickel hydrogen battery manufacturers, we are reaching beyond the space applications to the higher volume markets of aircraft starting and utility load leveling. Though space applications alone will not provide sufficient volume to support the economies of scale and opportunities for statistical process control, these additional terrestrial applications will. For example, nickel hydrogen batteries do not have the environmental problems of nickel cadmium or lead acid and may someday start your car or power your electric vehicle. However you envision the future, keep in mind that no manufacturer moves into a large volume market without fine tuning their process. The Center for Space Power at Texas A&M is providing indepth technical analysis of all of the materials and fabricated parts of our battery as well as thermal and mechanical design computer modeling. Several examples of what we are doing with nickel hydrogen chemistry to lead to these production efficiencies are presented.

Jones, Ken↗

NASA NTRSDate not supplied

Metal Additive Manufacturing for Rocket Engines: Successes and Failures

NASA has been developing additive manufacturing (AM) for various technical and programmatic advantages for complex rocket engine and aerospace components. This maturation has focused on various AM processes, new alloys, characterizing material properties, developing standards, producing demonstrator parts, and integrating AM hardware in liquid rocket engines through test-fail-fix cycles, as well as application and dissemination of lessons learned of the AM lifecycle. The importance of proper AM processing was made evident in the failure of a Laser Powder Bed Fusion (L-PBF) copper-alloy combustion chamber during a hot-fire test due to a degraded material quality. The hot-fire test aimed to demonstrate high duty cycle under a risk-tolerant development project, where consequences of component failure would be minimal. However, the unintentional component failure emphasized the necessity of robust material characterization and rigorous process control procedures for the safe use of AM components in critical applications. This presentation provides an overview of the development failure, a discussion on the evaluation of the failed chamber and supplemental chambers produced at the same time, a representative material samples that included intentional build witness lines, and a summary of the key results and recommendations from the evaluations. NASA continues to approach AM processes and designs with a level of risk and acceptance of failures that is appropriate for the project objectives, with the overall goal of safe implementation of AM technology and transferring AM technology into commercial space applications. This presentation with provide critical awareness to the community lessons learned on proper implementation of AM.

L-PBF↗

NASA NTRSDate not supplied

Successes and Failures of Metal Additive Manufacturing for Rocket Engines

NASA has been developing additive manufacturing (AM) for various technical and programmatic advantages for complex rocket engine and aerospace components. This maturation has focused on various AM processes, new alloys, characterizing material properties, developing standards, producing demonstrator parts, and integrating AM hardware in liquid rocket engines through test-fail-fix cycles, as well as application and dissemination of lessons learned of the AM lifecycle. The importance of proper AM processing was made evident in the failure of a Laser Powder Bed Fusion (L-PBF) copper-alloy combustion chamber during a hot-fire test due to a degraded material quality. The hot-fire test aimed to demonstrate high duty cycle under a risk-tolerant development project, where consequences of component failure would be minimal. However, the unintentional component failure emphasized the necessity of robust material characterization and rigorous process control procedures for the safe use of AM components in critical applications. This presentation provides an overview of the development failure, a discussion on the evaluation of the failed chamber and supplemental chambers produced at the same time, a representative material samples that included intentional build witness lines, and a summary of the key results and recommendations from the evaluations. NASA continues to approach AM processes and designs with a level of risk and acceptance of failures that is appropriate for the project objectives, with the overall goal of safe implementation of AM technology and transferring AM technology into commercial space applications. This presentation with provide critical awareness to the community lessons learned on proper implementation of AM.

Additive Manufacturing↗