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At least 145 records · Page 8

All sources NASA NTRS DOE OSTI

NASA NTRS2009

Integrated Vehicle Health Management Project-Modeling and Simulation for Wireless Sensor Applications

This paper describes the efforts in modeling and simulating electromagnetic transmission and reception as in a wireless sensor network through a realistic wing model for the Integrated Vehicle Health Management project at the Glenn Research Center. A computer model in a standard format for an S-3 Viking aircraft was obtained, converted to a Microwave Studio software format, and scaled to proper dimensions in Microwave Studio. The left wing portion of the model was used with two antenna models, one transmitting and one receiving, to simulate radio frequency transmission through the wing. Transmission and reception results were inconclusive.

Wallett, Thomas M.↗

NASA NTRS2019

Model-based System Health Management and Contingency Planning for Autonomous UAS

Safe autonomous operations of an Unmanned Aerial System (UAS) requires that the UAS can react to unforeseen circumstances, for example, after a failure has occurred. In this paper we describe a model-based run-time architecture for autonomous on-board diagnosis, system health management, and contingency management. This architecture is being instantiated on top of NASA's Core Flight System (cFS/cFE) as amajor component of the on-board AutonomousOperating System (AOS). We will describe our diagnosis and monitoring components, which continuously provide system health status. Automated reasoning with constraint satisfaction form the core of our decision-making component, which assesses the current situation, aids in failure disambiguation, and constructs a contingency plan to mitigate the failure(s) and allow for a safe end of the mission. We will illustrate our contingency management system with two case studies, one for a fixed-wing aircraft in simulation, and one for an autonomous DJI S1000+ octo-copter.

Schumann, Johann M.↗

NASA NTRS2010

Using the Integrated Vehicle Health Management Research Test and Integration Plan Wiki to Identify Synergistic Test Opportunities

The National Aeronautics and Space Administration (NASA) and the aviation industry have recognized a need for developing a method to identify and combine resources to carry out research and testing more efficiently. The Integrated Vehicle Health Management (IVHM) Research Test and Integration Plan (RTIP) Wiki is a tool that is used to visualize, plan, and accomplish collaborative research and testing. Synergistic test opportunities are developed using the RTIP Wiki, and include potential common resource testing that combines assets and personnel from NASA, industry, academia, and other government agencies. A research scenario is linked to the appropriate IVHM milestones and resources detailed in the wiki, reviewed by the research team members, and integrated into a collaborative test strategy. The scenario is then implemented by creating a test plan when appropriate and the research is performed. The benefits of performing collaborative research and testing are achieving higher Technology Readiness Level (TRL) test opportunities with little or no additional cost, improved quality of research, and increased communication among researchers. In addition to a description of the method of creating these joint research scenarios, examples of the successful development and implementation of cooperative research using the IVHM RTIP Wiki are given.

Koelfgen, Syri J.↗

NASA NTRS2013

Embedded diagnostic, prognostic, and health management system and method for a humanoid robot

A robotic system includes a humanoid robot with multiple compliant joints, each moveable using one or more of the actuators, and having sensors for measuring control and feedback data. A distributed controller controls the joints and other integrated system components over multiple high-speed communication networks. Diagnostic, prognostic, and health management (DPHM) modules are embedded within the robot at the various control levels. Each DPHM module measures, controls, and records DPHM data for the respective control level/connected device in a location that is accessible over the networks or via an external device. A method of controlling the robot includes embedding a plurality of the DPHM modules within multiple control levels of the distributed controller, using the DPHM modules to measure DPHM data within each of the control levels, and recording the DPHM data in a location that is accessible over at least one of the high-speed communication networks.

Barajas, Leandro G.↗

NASA NTRS2019

Health Management and Prognostics for Electric Aircraft Powertrain

W and c Any air borne vehicle needs incorporating safety as key parameter of measure, and inclusion of autonomy raises the critical need for safety under autonomous operations. Management of faults and component degradation is key as complexity in autonomous operations grow over the period of time. Therefore, in addition to basic operational requirements, an autonomous electric vehicle should be able to make accurate estimates of its current system health and take the correct decisions to complete its mission successfully. Real-time safety and state-awareness tools are therefore essential for the vehicle to be able to reach its destination in a safe and successful manner. The need for safety assurance and health management capabilities is particularly relevant for aircraft electric propulsion systems, which are relatively new and with limited historical to learn. They are critical systems requiring high power density along with reliability, resilience, efficient management of weight, and operational costs. A model- based fault diagnosis and prognostics approach of complex critical systems can successfully accomplish the safety and state awareness goal for such electric propulsion systems, enabling autonomous decision making capability for safe and efficient operation. To identify critical components in the system a Qualitative Bayesian approach using FMECA is implemented. This requires the assessment of some quantities representing the state of the electric unmanned aerial systems (e-UAS), as well as look-ahead forecasts of such states during the entire flight, presented in form of safety metrics (SM). In-service data and performance data gathered from degraded components sup- ports diagnostic and prognostic methods for these systems, but this data can be difficult to obtain as weight and packaging restrictions reduce redundancy and instrumentation on-board the vehicle. Therefore, an model-based framework should be capable or operating with limited data. In addition to data scarcity, the variability of such complex critical systems re- quires the model-based framework to reason in the presence of uncertainty, such as sensor noise, and modeling imperfections. Quantification of errors and uncertainties in the measured states and quantities is therefore a fundamental step for a precise estimation of such SMs; un-modeled uncertainty may result in erroneous state assessment and un- reliable predictions of future states of e-UAVs. Typical, centralized model-based schemes suffer from inherent disadvantages such as computational complexity, single point of failure, and scalability issues, and therefore may fail in such a complex scenario. This paper presents a methodology for developing a system level diagnostics and prognostics approach using a Qualitative Bayesian FMECA approach along with a formal uncertainty management framework for an e-UAS. In this work we demonstrate the efficacy of the framework to predict effects of sub-system level degradation on vehicle operation incorporating uncertainty management to predict future behavior under different operating conditions.

Kulkarni, Chetan↗

NASA NTRS2005

Health Management Applications for International Space Station

Traditional mission and vehicle management involves teams of highly trained specialists monitoring vehicle status and crew activities, responding rapidly to any anomalies encountered during operations. These teams work from the Mission Control Center and have access to engineering support teams with specialized expertise in International Space Station (ISS) subsystems. Integrated System Health Management (ISHM) applications can significantly augment these capabilities by providing enhanced monitoring, prognostic and diagnostic tools for critical decision support and mission management. The Intelligent Systems Division of NASA Ames Research Center is developing many prototype applications using model-based reasoning, data mining and simulation, working with Mission Control through the ISHM Testbed and Prototypes Project. This paper will briefly describe information technology that supports current mission management practice, and will extend this to a vision for future mission control workflow incorporating new ISHM applications. It will describe ISHM applications currently under development at NASA and will define technical approaches for implementing our vision of future human exploration mission management incorporating artificial intelligence and distributed web service architectures using specific examples. Several prototypes are under development, each highlighting a different computational approach. The ISStrider application allows in-depth analysis of Caution and Warning (C&W) events by correlating real-time telemetry with the logical fault trees used to define off-nominal events. The application uses live telemetry data and the Livingstone diagnostic inference engine to display the specific parameters and fault trees that generated the C&W event, allowing a flight controller to identify the root cause of the event from thousands of possibilities by simply navigating animated fault tree models on their workstation. SimStation models the functional power flow for the ISS Electrical Power System and can predict power balance for nominal and off-nominal conditions. SimStation uses realtime telemetry data to keep detailed computational physics models synchronized with actual ISS power system state. In the event of failure, the application can then rapidly diagnose root cause, predict future resource levels and even correlate technical documents relevant to the specific failure. These advanced computational models will allow better insight and more precise control of ISS subsystems, increasing safety margins by speeding up anomaly resolution and reducing,engineering team effort and cost. This technology will make operating ISS more efficient and is directly applicable to next-generation exploration missions and Crew Exploration Vehicles.

Alena, Richard↗

NASA NTRS2005

Smart Sensors' Role in Integrated System Health Management

During the last decade, there has been a major effort in the aerospace industry to reduce the cost per pond of payload and become competitive in the international market. Competition from Europe, Japan, and China has reduced this cost to almost a third from 1990 to 2000. This cost has leveled in recent years to an average price of around $12,000/pound of payload. One of NASA's goals is to promote the development of technologies to reduce this cost by a factor of 10 or more Exploration of space, specially manned exploration missions, involves very complex launch and flight vehicles, associated ground support systems, and extensive human support during all phases of the mission. When considering the Space Shuttle Program, we can see that vehicle and ground support systems' processing, operation, and maintenance represent a large percentage of the program cost and time. Reducing operating, processing and maintenance costs will greatly reduce the cost of Exploration programs. The Integrated System Health Management (ISHM) concept is one of the technologies that will help reduce these operating, processing and maintenance costs. ISHM is an integrated health monitoring system applicable to both flight and ground systems. It automatically and autonomously acquires information from sensors and actuators and processes that information using the ISHM-embedded knowledge. As a result, it establishes the health of the system based on the acquired information and its prior knowledge. When this concept is fully implemented, ISHM systems shall be able to perform failure prediction and remediation before actual hard failures occurs, preventing its costly consequences. Data sources, sensors, and their associated data acquisition systems, constitute the foundation of the system. A smart sensing architecture is required to support the acquisition of reliable, high quality data, required by the ISHM. A thorough definition of the smart sensor architectures, their embedded diagnostic agents, and communication protocols need to be established and standardized to allow the embedding and exchange of health information among sensors and ISHM. This workshop is aimed to foster the exchange of ideas and lessons learned between government, industry and academia to aid in the establishment of ISHM (and smart sensors) standards and guidelines as well as to identify present technology gaps that will have to be overcome to successfully achieve this goal.

Perotti, Jose M.↗

DOE OSTI2015

Prognostic and health management of active assets in nuclear power plants

This study presents the development of diagnostic and prognostic capabilities for active assets in nuclear power plants (NPPs). The research was performed under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program. Idaho National Laboratory researched, developed, implemented, and demonstrated diagnostic and prognostic models for generator step-up transformers (GSUs). The Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software developed by the Electric Power Research Institute was used to perform diagnosis and prognosis. As part of the research activity, Idaho National Laboratory implemented 22 GSU diagnostic models in the Asset Fault Signature Database and two wellestablished GSU prognostic models for the paper winding insulation in the Remaining Useful Life Database of the FW-PHM Suite. The implemented models along with a simulated fault data stream were used to evaluate the diagnostic and prognostic capabilities of the FW-PHM Suite. Knowledge of the operating condition of plant asset gained from diagnosis and prognosis is critical for the safe, productive, and economical long-term operation of the current fleet of NPPs. This research addresses some of the gaps in the current state of technology development and enables effective application of diagnostics and prognostics to nuclear plant assets.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2014

Progress Towards Prognostic Health Management of Passive Components in Advanced Small Modular Reactors

Sustainable nuclear power to promote energy security and to reduce greenhouse gas emissions are two key national energy priorities. The development of deployable small modular reactors (SMRs) is expected to support these objectives by developing technologies that improve the reliability, sustain safety, and improve affordability of new reactors. Advanced SMRs (AdvSMRs) refer to a specific class of SMRs and are based on modularization of advanced reactor concepts. Prognostic health management (PHM) systems can benefit both the safety and economics of deploying AdvSMRs and can play an essential role in managing the inspection and maintenance of passive components in AdvSMR systems. This paper describes progress on development of a prototypic PHM system for AdvSMR passive components, with thermal creep chosen as the target degradation mechanism.

prognostics, small modular reactors, passive compo↗

NASA NTRS2002

Propulsion Controls and Health Management Research at NASA Glenn Research Center

Garg, Sanjay↗

NASA NTRS2004

Propulsion Control and Health Management (PCHM) Technology for Flight Test on the C-17 T-1 Aircraft

The C-I 7 T-l Globemaster III is an Air Force flight research vehicle located at Edwards Air Force Base. NASA Dryden and the C-17 System Program Office have entered into a Memorandum of Agreement to permit NASA the use of the C-I 7 T-I to conduct flight research on a mutually coordinated schedule. The C-17 Propulsion Control and Health Management (PCHM) Working Group was formed in order to foster discussion and coordinate planning amongst the various government agencies conducting PCHM research with a potential need for flight testing, and to communicate to the PCHM community the capabilities of the C-17 T-l aircraft to support such flight testing. This paper documents the output of this Working Group, including a summary of the candidate PCHM technologies identified and their associated benefits relative to NASA goals and objectives.

Simon, Donald L.↗

NASA NTRS2016

Strain-Based Damage Determination Using Finite Element Analysis for Structural Health Management

A damage determination method is presented that relies on in-service strain sensor measurements. The method employs a gradient-based optimization procedure combined with the finite element method for solution to the forward problem. It is demonstrated that strains, measured at a limited number of sensors, can be used to accurately determine the location, size, and orientation of damage. Numerical examples are presented to demonstrate the general procedure. This work is motivated by the need to provide structural health management systems with a real-time damage characterization. The damage cases investigated herein are characteristic of point-source damage, which can attain critical size during flight. The procedure described can be used to provide prognosis tools with the current damage configuration.

Hochhalter, Jacob D.↗

NASA NTRS2005

Advanced Health Management Algorithms for Crew Exploration Applications

Achieving the goals of the President's Vision for Exploration will require new and innovative ways to achieve reliability increases of key systems and sub-systems. The most prominent approach used in current systems is to maintain hardware redundancy. This imposes constraints to the system and utilizes weight that could be used for payload for extended lunar, Martian, or other deep space missions. A technique to improve reliability while reducing the system weight and constraints is through the use of an Advanced Health Management System (AHMS). This system contains diagnostic algorithms and decision logic to mitigate or minimize the impact of system anomalies on propulsion system performance throughout the powered flight regime. The purposes of the AHMS are to increase the probability of successfully placing the vehicle into the intended orbit (Earth, Lunar, or Martian escape trajectory), increase the probability of being able to safely execute an abort after it has developed anomalous performance during launch or ascent phases of the mission, and to minimize or mitigate anomalies during the cruise portion of the mission. This is accomplished by improving the knowledge of the state of the propulsion system operation at any given turbomachinery vibration protection logic and an overall system analysis algorithm that utilizes an underlying physical model and a wide array of engine system operational parameters to detect and mitigate predefined engine anomalies. These algorithms are generic enough to be utilized on any propulsion system yet can be easily tailored to each application by changing input data and engine specific parameters. The key to the advancement of such a system is the verification of the algorithms. These algorithms will be validated through the use of a database of nominal and anomalous performance from a large propulsion system where data exists for catastrophic and noncatastrophic propulsion sytem failures.

Davidson, Matt↗

NASA NTRS2004

Controls and Health Management Technologies for Intelligent Aerospace Propulsion Systems

With the increased emphasis on aircraft safety, enhanced performance and affordability, and the need to reduce the environmental impact of aircraft, there are many new challenges being faced by the designers of aircraft propulsion systems. The Controls and Dynamics Technology Branch at NASA (National Aeronautics and Space Administration) Glenn Research Center (GRC) in Cleveland, Ohio, is leading and participating in various projects in partnership with other organizations within GRC and across NASA, the U.S. aerospace industry, and academia to develop advanced controls and health management technologies that will help meet these challenges through the concept of an Intelligent Engine. The key enabling technologies for an Intelligent Engine are the increased efficiencies of components through active control, advanced diagnostics and prognostics integrated with intelligent engine control to enhance component life, and distributed control with smart sensors and actuators in an adaptive fault tolerant architecture. This paper describes the current activities of the Controls and Dynamics Technology Branch in the areas of active component control and propulsion system intelligent control, and presents some recent analytical and experimental results in these areas.

Garg, Sanjay↗

NASA NTRS2015

Towards Real-Time, On-Board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

For unmanned aerial systems (UAS) to be successfully deployed and integrated within the national airspace, it is imperative that they possess the capability to effectively complete their missions without compromising the safety of other aircraft, as well as persons and property on the ground. This necessity creates a natural requirement for UAS that can respond to uncertain environmental conditions and emergent failures in real-time, with robustness and resilience close enough to those of manned systems. We introduce a system that meets this requirement with the design of a real-time onboard system health management (SHM) capability to continuously monitor sensors, software, and hardware components. This system can detect and diagnose failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the-fly temporal and Bayesian probabilistic fault diagnosis; and (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software. We call this approach rt-R2U2, a name derived from its requirements. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual flight data from the NASA Swift UAS.

Unmanned Aerial System↗

DOE OSTI2015

Prognostics Health Management Model for LED Package Failure Under Contaminated Environment

The reliability consideration of LED products includes both luminous flux drop and color shift. Previous research either talks about luminous maintenance or color shift, because luminous flux degradation usually takes very long time to observe. In this paper, the impact of a VOC (volatile organic compound) contaminated luminous flux and color stability are examined. As a result, both luminous degradation and color shift had been recorded in a short time. Test samples are white, phosphor-converted, high-power LED packages. Absolute radiant flux is measured with integrating sphere system to calculate the luminous flux. Luminous flux degradation and color shift distance were plotted versus aging time to show the degradation pattern. A prognostic health management (PHM) method based on the state variables and state estimator have been proposed in this paper. In this PHM framework, unscented kalman filter (UKF) was deployed as the carrier of all states. During the estimation process, third order dynamic transfer function was used to implement the PHM framework. Both of the luminous flux and color shift distance have been used as the state variable with the same PHM framework to exam the robustness of the method. Predicted remaining useful life is calculated at every measurement point to compare with the tested remaining useful life. The result shows that state estimator can be used as the method for the PHM of LED degradation with respect to both luminous flux and color shift distance. The prediction of remaining useful life of LED package, made by the states estimator and data driven approach, falls in the acceptable error-bounds (20%) after a short training of the estimator.

Lall, Pradeep↗

NASA NTRS2012

A Brief Review of the Need for Robust Smart Wireless Sensor Systems for Future Propulsion Systems, Distributed Engine Controls, and Propulsion Health Management

Smart Sensor Systems with wireless capability operational in high temperature, harsh environments are a significant component in enabling future propulsion systems to meet a range of increasingly demanding requirements. These propulsion systems must incorporate technology that will monitor engine component conditions, analyze the incoming data, and modify operating parameters to optimize propulsion system operations. This paper discusses the motivation towards the development of high temperature, smart wireless sensor systems that include sensors, electronics, wireless communication, and power. The challenges associated with the use of traditional wired sensor systems will be reviewed and potential advantages of Smart Sensor Systems will be discussed. A brief review of potential applications for wireless smart sensor networks and their potential impact on propulsion system operation, with emphasis on Distributed Engine Control and Propulsion Health Management, will be given. A specific example related to the development of high temperature Smart Sensor Systems based on silicon carbide electronics will be discussed. It is concluded that the development of a range of robust smart wireless sensor systems are a foundation for future development of intelligent propulsion systems with enhanced capabilities.

Hunter, Gary W.↗

NASA NTRSDate not supplied

Physics Informed Neural Nets for Systems Health Management

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Development in data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. The research work presents application of physics-informed neural nets application to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage.

Physics Informed↗