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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

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NASA NTRS1998

Automated System Checkout to Support Predictive Maintenance for the Reusable Launch Vehicle

The Propulsion Checkout and Control System (PCCS) is a predictive maintenance software system. The real-time checkout procedures and diagnostics are designed to detect components that need maintenance based on their condition, rather than using more conventional approaches such as scheduled or reliability centered maintenance. Predictive maintenance can reduce turn-around time and cost and increase safety as compared to conventional maintenance approaches. Real-time sensor validation, limit checking, statistical anomaly detection, and failure prediction based on simulation models are employed. Multi-signal models, useful for testability analysis during system design, are used during the operational phase to detect and isolate degraded or failed components. The TEAMS-RT real-time diagnostic engine was developed to utilize the multi-signal models by Qualtech Systems, Inc. Capability of predicting the maintenance condition was successfully demonstrated with a variety of data, from simulation to actual operation on the Integrated Propulsion Technology Demonstrator (IPTD) at Marshall Space Flight Center (MSFC). Playback of IPTD valve actuations for feature recognition updates identified an otherwise undetectable Main Propulsion System 12 inch prevalve degradation. The algorithms were loaded into the Propulsion Checkout and Control System for further development and are the first known application of predictive Integrated Vehicle Health Management to an operational cryogenic testbed. The software performed successfully in real-time, meeting the required performance goal of 1 second cycle time.

Patterson-Hine, Ann↗

NASA NTRS1972

Demonstration of the use of ADAPT to derive predictive maintenance algorithms for the KSC central heat plant

The Avco Data Analysis and Prediction Techniques (ADAPT) were employed to determine laws capable of detecting failures in a heat plant up to three days in advance of the occurrence of the failure. The projected performance of algorithms yielded a detection probability of 90% with false alarm rates of the order of 1 per year for a sample rate of 1 per day with each detection, followed by 3 hourly samplings. This performance was verified on 173 independent test cases. The program also demonstrated diagnostic algorithms and the ability to predict the time of failure to approximately plus or minus 8 hours up to three days in advance of the failure. The ADAPT programs produce simple algorithms which have a unique possibility of a relatively low cost updating procedure. The algorithms were implemented on general purpose computers at Kennedy Space Flight Center and tested against current data.

Hunter, H. E.↗

NASA NTRS1989

Predictive Attitude Maintenance For A Space Station

Paper provides mathematical basis for predictive management of angular momenta of control-moment gyroscopes (CMG's) to control attitude of orbiting space station. Numerical results presented for pitch control of proposed power-tower space station. Based on prior orbit history and mathematical model of density of atmosphere, predictions made of requirements on dumping and storage of angular momentum in relation to current loading state of CMG's and to acceptable attitude tolerances.

Hattis, Philip D.↗

NASA NTRS2023

An Analysis of Barriers Preventing the Widespread Adoption of Predictive and Prescriptive Maintenance in Aviation

The aviation industry has long recognized the potential benefits of predictive maintenance, a maintenance strategy that leverages sensor and operational data to predict the future degradation of components. Prescriptive maintenance takes this a step further and considers the entire aviation ecosystem to schedule maintenance actions optimally. With the ability to reduce maintenance costs by up to 30%, as reported by the Department of Energy, these maintenance strategies have been identified to be an important investment to reduce a airline costs. However, despite great interest and technological advances in areas such as diagnostics, prognostics, sensing, computation, and machine learning, the adoption of predictive and prescriptive maintenance has not been widely applied in aviation. To shed light on this issue, we conducted an analysis of the barriers preventing or limiting the adoption of predictive and prescriptive maintenance in aviation. Through discussions with subject matter experts across industry, academia, standards bodies, and government, we identified five key challenges: complexity of prediction; validation, safety assurance, and regulatory challenges; cost of adoption; difficulty in quantifying impact and informing decisions; and data availability, quality, and ownership challenges. This study provides a detailed overview of these barriers and areas where stakeholders could invest to overcome them, aiming to support the scaled adoption of predictive and prescriptive maintenance in aviation.

Christopher Teubert↗

NASA NTRS1987

Logistics and operations integration requirements to support Space Station servicing of free flying spacecraft - OMV flight operation

The logistics of OMV free-flyer servicing are examined, with emphasis on integrating the OMV operations into the overall STS-Space Station system. The depletion rate of consumables and lifetimes of free-flyer components are known quantities, which permits definition of a predictable maintenance schedule. Servicing with an OMV will depend on the position and capabilities of the OMV, Shuttle and Station when free-flyer maintenance is needed. Optimized orbital servicing of free-flyers will involve coordination of and resolution of schedule conflicts among STS, the OMV and the Station. The scheduled availability of any of the three components will be predicted in terms of probabilities that any one of the components will not be needed for another mission while performing the mission they are on.

Bell, Jerome A.↗

NASA NTRS1990

Attitude Determination and Control System (ADCS) and Maintenance and Diagnostic System (MDS): A maintenance and diagnostic system for Space Station Freedom

The Maintenance and Diagnostic System (MDS) that is being developed at Honeywell to enhance the Fault Detection Isolation and Recovery system (FDIR) for the Attitude Determination and Control System on Space Station Freedom is described. The MDS demonstrates ways that AI-based techniques can be used to improve the maintainability and safety of the Station by helping to resolve fault anomalies that cannot be fully determined by built-in-test, by providing predictive maintenance capabilities, and by providing expert maintenance assistance. The MDS will address the problems associated with reasoning about dynamic, continuous information versus only about static data, the concerns of porting software based on AI techniques to embedded targets, and the difficulties associated with real-time response. An initial prototype was built of the MDS. The prototype executes on Sun and IBM PS/2 hardware and is implemented in the Common Lisp; further work will evaluate its functionality and develop mechanisms to port the code to Ada.

Toms, David↗

NASA NTRS1991

Automated Predictive Diagnosis (APD): A 3-tiered shell for building expert systems for automated predictions and decision making

The APD software features include: On-line help, Three level architecture, (Logic environments, Setup/Application environment, Data environment), Explanation capability, and File handling. The kinds of experimentation and record keeping that leads to effective expert systems is facilitated by: (1) a library of inferencing modules (in the logic environment); (2) an explanation capability which reveals logic strategies to users; (3) automated file naming conventions; (4) an information retrieval system; and (5) on-line help. These aid with effective use of knowledge, debugging and experimentation. Since the APD software anticipates the logical rules becoming complicated, it is embedded in a production system language (CLIPS) to insure the full power of the production system paradigm of CLIPS and availability of the procedural language C. The development is discussed of the APD software and three example applications: toy, experimental, and operational prototype for submarine maintenance predictions.

Steib, Michael↗

NASA NTRSDate not supplied

Predicting Adverse Events and their Precursors in Aviation Using Multi-Class Multiple-Instance Learning

In recent years, there has been a rapid growth in the application of machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications. However, this paper focuses on the identification of precursors, which is a relatively newer application. Precursors are events correlated with adverse events that happen prior to the adverse event itself. Therefore, precursor mining provides many benefits including understanding the reasons behind a safety incident and the ability to identify signatures, which can be tracked throughout a flight to alert the operators of an potential upcoming adverse event. This work proposes using the multiple-instance learning (MIL) framework, a weakly supervised learning task, combined with a carefully designed Multi-Head Convolutional Neural Networks-Recurrent Neural Networks (MHCNN-RNN) architecture to predict different type of adverse events for any given flights and identify their precursors with little to no post-processing.Results obtained show that the MHCNN-RNN is able to accurately forecast high speed and high path angle events during the approach, and that it is also capable of determining the aircraft’s parameters that are correlated to these events. These parameters can be considered precursors to the events.

multiple instance learning↗

NASA NTRS2006

Comprehensive Environmental Informatics System (CEIS) Integrating Crew and Vehicle Environmental Health

Integrated Vehicle Health Management (IVHM) systems have been pursued as highly integrated systems that include smart sensors, diagnostic and prognostics software for assessments of real-time and life-cycle vehicle health information. Inclusive to such a system is the requirement to monitor the environmental health within the vehicle and the occupants of the vehicle. In this regard an enterprise approach to informatics is used to develop a methodology entitled, Comprehensive Environmental Informatics System (CEIS). The hardware and software technologies integrated into this system will be embedded in the vehicle subsystems, and maintenance operations, to provide both real-time and life-cycle health information of the environment within the vehicle cabin and of its occupants. This comprehensive information database will enable informed decision making and logistics management. One key element of the CEIS is interoperability for data acquisition and archive between environment and human system monitoring. With comprehensive components the data acquired in this system will use model based reasoning systems for subsystem and system level managers, advanced on-board and ground-based mission and maintenance planners to assess system functionality. Knowledge databases of the vehicle health state will be continuously updated and reported for critical failure modes, and routinely updated and reported for life cycle condition trending. Sufficient intelligence, including evidence-based engineering practices which are analogous to evidencebased medicine practices, will be included in the CEIS to result in more rapid recognition of off-nominal operation to enable quicker corrective actions. This will result from better information (rather than just data) for improved crew/operator situational awareness, which will produce significant vehicle and crew safety improvements, as well as increasing the chance for mission success, future mission planning as well as training. Other benefits include improved reliability, increase safety in operations and cost of operations. The cost benefits stem from significantly reduced processing and operations manpower, predictive maintenance for systems and subjects. The improvements in vehicle functionality and cost will result from increased prognostic and diagnostic capability due to the detailed total human exploration system health knowledge from CEIS. A collateral benefit is that there will be closer observations of the vehicle occupants as wrist watch sized devices are worn for continuous health monitoring. Additional database acquisition will stem from activities in countermeasure practices to ensure peak performance capability by occupants of the vehicle. The CEIS will provide data from advanced sensing technologies and informatics modeling which will be useful in problem troubleshooting, and improving NASA s awareness of systems during operation.

Nall, Mark E.↗

NASA NTRS1973

Assuring reliability program effectiveness.

An attempt is made to provide simple identification and description of techniques that have proved to be most useful either in developing a new product or in improving reliability of an established product. The first reliability task is obtaining and organizing parts failure rate data. Other tasks are parts screening, tabulation of general failure rates, preventive maintenance, prediction of new product reliability, and statistical demonstration of achieved reliability. Five principal tasks for improving reliability involve the physics of failure research, derating of internal stresses, control of external stresses, functional redundancy, and failure effects control. A final task is the training and motivation of reliability specialist engineers.

Ball, L. W.↗

NASA NTRS2010

Fault Management Technology Maturation for NASA's Constellation Program

This slide presentation reviews the maturation of fault management technology in preparation for the Constellation Program. There is a review of the Space Shuttle Main Engine (SSME) and a discussion of a couple of incidents with the shuttle main engine and tanking that indicated the necessity for predictive maintenance. Included is a review of the planned Ares I-X Ground Diagnostic Prototype (GDP) and further information about detection and isolation of faults using Testability Engineering and Maintenance System (TEAMS). Another system that being readied for use that detects anomalies, the Inductive Monitoring System (IMS). The IMS automatically learns how the system behaves and alerts operations it the current behavior is anomalous. The comparison of STS-83 and STS-107 (i.e., the Columbia accident) is shown as an example of the anomaly detection capabilities.

Waterman, Robert D.↗

NASA NTRSDate not supplied

NASA’s EGS Program Technology Demonstrations and Risk Reduction for Sustainable Surface Operations

This paper discusses NASA’s Exploration Ground Systems (EGS) Program Technology Infusion Strategy and the results from EGS technology demonstration projects, including Integrated Health Management capabilities for Condition Based and Predictive Maintenance, conservation of cryogenic fluids, and logistics management/commonality of maintenance equipment, tools, and consumables. The paper also discusses how technologies and capabilities implemented for ground systems (i.e. automated/autonomous operations and maintenance) can be leveraged to help reduce risk for sustainable lunar surface operations.

Launch Processing↗

NASA NTRSDate not supplied

NASA’s Exploration Ground Systems Program: Technology Demonstrations and Risk Reduction for Sustainable Surface Operations

This presentation discusses NASA’s Exploration Ground Systems (EGS) Program Technology Infusion Strategy and the results from EGS technology demonstration projects, including Integrated Health Management capabilities for Condition Based and Predictive Maintenance, conservation of cryogenic fluids, and logistics management/commonality of maintenance equipment, tools, and consumables. The presentation also discusses how technologies and capabilities implemented for ground systems (i.e. automated/ autonomous operations and maintenance) can be leveraged to help reduce risk for sustainable lunar surface operations.

Technology Infusion↗

NASA NTRSDate not supplied

ECLSS Four-Bed CO2 Scrubber Digital Twin

The ECLSS Digital Twin is a cloud-based simulation of the Four-Bed CO2 Scrubber, currently one of the primary means of removing carbon dioxide onboard the International Space Station, that operationalizes SME-developed multi-physics models and replicates conditions of the actual hardware in near real-time. In addition to providing insight into the system’s performance, it will enable prognostics, diagnostics, predictive maintenance, and simulated off-nominal scenarios. By leveraging digital representations, projects can save resources and gain a better understanding of their physical systems with the goal of building robust and reliable hardware for future deep space exploration. The use of data infrastructure in the cloud allows for streamlined analysis and visualization on a much larger scale than is possible with current tools.

Jared Wilson↗

NASA NTRS1995

Integrated Logistics Support Analysis of the International Space Station Alpha: An Overview of the Maintenance Time Dependent Parameter Prediction Methods Enhancement

The objective of this publication is to introduce the enhancement methods for the overall reliability and maintainability methods of assessment on the International Space Station. It is essential that the process to predict the values of the maintenance time dependent variable parameters such as mean time between failure (MTBF) over time do not in themselves generate uncontrolled deviation in the results of the ILS analysis such as life cycle costs, spares calculation, etc. Furthermore, the very acute problems of micrometeorite, Cosmic rays, flares, atomic oxygen, ionization effects, orbital plumes and all the other factors that differentiate maintainable space operations from non-maintainable space operations and/or ground operations must be accounted for. Therefore, these parameters need be subjected to a special and complex process. Since reliability and maintainability strongly depend on the operating conditions that are encountered during the entire life of the International Space Station, it is important that such conditions are accurately identified at the beginning of the logistics support requirements process. Environmental conditions which exert a strong influence on International Space Station will be discussed in this report. Concurrent (combined) space environments may be more detrimental to the reliability and maintainability of the International Space Station than the effects of a single environment. In characterizing the logistics support requirements process, the developed design/test criteria must consider both the single and/or combined environments in anticipation of providing hardware capability to withstand the hazards of the International Space Station profile. The effects of the combined environments (typical) in a matrix relationship on the International Space Station will be shown. The combinations of the environments where the total effect is more damaging than the cumulative effects of the environments acting singly, may include a combination such as temperature, humidity, altitude, shock, and vibration while an item is being transported. The item's acceptance to its end-of-life sequence must be examined for these effects.

Sepehry-Fard, F.↗

NASA NTRS1979

Maintenance cost study of rotary wing aircraft, phase 2

The Navy's maintenance and materials management data base was used in a study to determine the feasibility of predicting unscheduled maintenance costs for the dynamic systems of military rotary wing aircraft. The major operational and design variables were identified and the direct maintenance man hours per flight hour were obtained by step-wise multiple regression analysis. Five nonmilitary helicopter users were contacted to supply data on which variables were important factors in civil applications. These uses included offshore oil exploration and support, police and fire department rescue and enforcement, logging and heavy equipment movement, and U.S. Army military operations. The equations developed were highly effective in predicting unscheduled direct maintenance man hours per flying hours for military aircraft, but less effective for commercial or public service helicopters, probably because of the longer mission durations and the much higher utilization of civil users.

Source record↗

NASA NTRS1996

Understanding and Predicting the Process of Software Maintenance Releases

One of the major concerns of any maintenance organization is to understand and estimate the cost of maintenance releases of software systems. Planning the next release so as to maximize the increase in functionality and the improvement in quality are vital to successful maintenance management. The objective of this paper is to present the results of a case study in which an incremental approach was used to better understand the effort distribution of releases and build a predictive effort model for software maintenance releases. This study was conducted in the Flight Dynamics Division (FDD) of NASA Goddard Space Flight Center(GSFC). This paper presents three main results: 1) a predictive effort model developed for the FDD's software maintenance release process; 2) measurement-based lessons learned about the maintenance process in the FDD; and 3) a set of lessons learned about the establishment of a measurement-based software maintenance improvement program. In addition, this study provides insights and guidelines for obtaining similar results in other maintenance organizations.

Basili, Victor↗

NASA NTRS2017

Implementation and Integration of a Finite Element Model into the Bone Remodeling Model to Characterize Skeletal Loading

NASA's Digital Astronaut Project is developing a bone physiology model to predict changes in bone mineral density over the course of a space mission. The model intends to predict bone loss due to exposure in microgravity as well as predicting bone maintenance due to mechanical stimulus generated by exercise countermeasures. These predictions will be used to inform exercise device efficacy and to help design exercise protocols that will maintain bone mineral density during long exposures to microgravity during spaceflight. The mechanical stimulus and the stresses that are exhibited on the bone are important factors for bone remodeling. These stresses are dependent on the types of exercise that are performed and vary throughout the bone due to the geometry. A primary area of focus for bone health is the proximal femur. This location is critical in transmitting loads between the upper and lower body and have been known to be a critical failure point in older individuals with conditions like osteoporosis.

bone mineral content↗