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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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NASA NTRS1997

Structural Analysis of the QCM Aboard the ER-2

As a result of recent supersonic transport (SST) studies on the effect they may have on the atmosphere, several experiments have been proposed to capture and evaluate samples of the stratosphere where SST's travel. One means to achieve this is to utilize the quartz crystal microbalance (QCM) installed aboard the ER-2, formerly the U-2 reconnaissance aircraft. The QCM is a cascade impactor designed to perform in-situ, real-time measurements of aerosols and chemical vapors at an altitude of 60,000 - 70,000 feet. The ER-2 is primarily used by NASA for Earth resources to test new sensor systems before they are placed aboard satellites. One of the main reasons the ER-2 is used for this flight experiment is its capability to fly approximately twelve miles above sea level (can reach an altitude of 78,000 feet). Because the ER-2 operates at such a high altitude, it is of special interest to scientists interested in space exploration or supersonic aircraft. Some of the experiments are designed to extract data from the atmosphere around the ER-2. For the current flight experiment, the QCM is housed in a frame that is connected to an outer pod that is attached to the fuselage of the ER-2. Due to the location of the QCM within the housing frame and the location of the pod on the ER-2, the pod and its contents are subject to structural loads. In addition to structural loads, structural vibrations are also of importance because the QCM is a frequency induced instrument. Therefore, a structural analysis of the instrument within the frame is imperative to determine if resonance and/or undesirable deformations occur.

Jones, Phyllis D.↗
NASA NTRS1995

AstroNet: A Tool Set for Simultaneous, Multi-Site Observations of Astronomical Objects

Earth-based, fully automatic "robotic" telescopes have been in routine operation for a number of years. As their number grows and their distribution becomes global, increasing attention is being given to forming networks of various sorts that will allow them, as a group, to make observations 24 hours a day in both hemispheres. We have suggested that telescopes based in space be part of this network. We further suggested that any telescope on this network be capable of asking, almost in real time, that other robotic telescopes perform support observations for them. When a target of opportunity required support observations, the system would determine which telescope(s) in the network would be most appropriate to make the observations and formulate a request to do so. Because the network would be comprised of telescopes located in widely distributed regions, this system would guarantee continuity of observations This report summarizes our efforts under this contract. We proposed to develop a set of data collection and display tools to aid simultaneous observation of astronomical targets from a number of observing sites. We planned to demonstrate the usefulness of this toolset for simultaneous multi-site observation of astronomical targets. Possible candidates or the proposed demonstration included the Extreme Ultraviolet Explorer (EUVE), International Ultraviolet Explorer (IUE), and ALEXIS, sounding rocket experiments. Ground-based observatories operated by the University of California, Berkeley, the Jet Propulsion Laboratory, and Fairborn Observatory in Mesa, Arizona were to be used to demonstrate the proposed concept. Although the demonstration was to have involved astronomical investigations, the tools were to have been applicable to a large number of scientific disciplines. The software tools and systems developed as a result of the work were to have been made available to the scientific community.

Chakrabarti, Supriya↗
NASA NTRS2013

An Alternative Flight Software Trigger Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions using Inaccurate or Scarce Information

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter. In order to increase overall robustness, the vehicle also has an alternate method of triggering the drogue parachute deployment based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this velocity-based trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers excellent performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

Smith, Kelly M.↗
NASA NTRS2013

An Alternative Flight Software Trigger Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions Using Inaccurate or Scarce Information

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

Smith, Kelly M.↗
NASA NTRS2013

An Alternative Flight Software Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions using Inaccurate or Scarce Information

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles

Smith, Kelly↗
NASA NTRS2015

Double-Diffusive Convection During Growth of Halides and Selenides

Heavy metal halides and selenides have unique properties which make them excellent materials for chemical, biological and radiological sensors. Recently it has been shown that selenohalides are even better materials than halides or selenides for gamma-ray detection. These materials also meet the strong needs of a wide band imaging technology to cover ultra-violet (UV), midwave infrared wavelength (MWIR) to very long wavelength infrared (VLWIR) region for hyperspectral imager components such as etalon filters and acousto-optic tunable filters (AO). In fact AOTF based imagers based on these materials have some superiority than imagers based on liquid crystals, FTIR, Fabry-Perot, grating, etalon, electro-optic modulation, piezoelectric and several other concepts. For example, broadband spectral and imagers have problems of processing large amount of information during real-time observation. Acousto-Optic Tunable Filter (AOTF) imagers are being developed to fill the need of reducing processing time of data, low cost operation and key to achieving the goal of covering long-wave infrared (LWIR). At the present time spectral imaging systems are based on the use of diffraction gratings are typically used in a pushbroom or whiskbroom mode. They are mostly used in systems and acquire large amounts of hyperspectral data that is processed off-line later. In contrast, acousto-optic tunable filter spectral imagers require very little image processing, providing new strategies for object recognition and tracking. They are ideally suited for tactical situations requiring immediate real-time image processing. But the performance of these imagers depends on the quality and homogeneity of acousto-optic materials. In addition for many systems requirements are so demanding that crystals up to sizes of 10 cm length are desired. We have studied several selenides and halide crystals for laser and AO imagers for MWIR and LWIR wavelength regions. We have grown and fabricated crystals of several materials such as mercurous chloride, mercurous bromide, mercurous iodide, lead chloride lead bromide, lead iodide, thallium arsenic selenide, gallium selenide, zince sulfide zinc selenide and several crystals into devices. We have used both Bridgman and physical vapor transport (PVT) crystal growth methods. In the past have examined PVT growth numerically for conditions where the boundary of the enclosure is subjected to a nonlinear thermal profile. Since past few months we have been working on binary and ternary materials such as selenoiodides, doped zinc sulfides and mercurous chloro bromide and mercurous bromoiodides. In the doped and ternary materials thermal and solutal convection play extremely important role during the growth. Very commonly striations and banding is observed. Our experiments have indicated that even in highly purified source materials, homogeneity in 1-g environment is very difficult. Some of our previous numerical studies have indicated that gravity level less than 10-4 (-g) helps in controlling the thermosolutal convection. We will discuss the ground based growth results of HgClxBr(1-x) and ZnSe growth results for the mm thick to large cm size crystals. These results will be compared with our microgravity experiments performed with this class of materials. For both HgCl-HgBr and ZnS-ZnSe the lattice parameters of the mixtures obey Vagard's law in the studied composition range. The study demonstrates that properties are very anisotropic with crystal orientation, and performance achievement requires extremely careful fabrication to utilize highest figure of merit. In addition, some parameters such as crystal growth fabrication, processing time, resolution, field of view and efficiency will be described based on novel solid solution materials. It was predicted that very similar to the pure compounds solid solutions also have very large anisotropy, and very precise oriented and homogeneous bulk and thin film crystals is required to achieve maximum performance of laser or imagers. Some of the parameters controlling the homogeneity such as thermos-solutal convection driven forces can be controlled in microgravity environments to utilize the benefits of these unique materials.

Singh, N. B.↗
NASA NTRS2016

Development of an Objective Space Suit Mobility Performance Metric Using Metabolic Cost and Functional Tasks

Existing methods for evaluating EVA suit performance and mobility have historically concentrated on isolated joint range of motion and torque. However, these techniques do little to evaluate how well a suited crewmember can actually perform during an EVA. An alternative method of characterizing suited mobility through measurement of metabolic cost to the wearer has been evaluated at Johnson Space Center over the past several years. The most recent study involved six test subjects completing multiple trials of various functional tasks in each of three different space suits; the results indicated it was often possible to discern between different suit designs on the basis of metabolic cost alone. However, other variables may have an effect on real-world suited performance; namely, completion time of the task, the gravity field in which the task is completed, etc. While previous results have analyzed completion time, metabolic cost, and metabolic cost normalized to system mass individually, it is desirable to develop a single metric comprising these (and potentially other) performance metrics. This paper outlines the background upon which this single-score metric is determined to be feasible, and initial efforts to develop such a metric. Forward work includes variable coefficient determination and verification of the metric through repeated testing.

Norcross, J. R.↗
NASA NTRS2016

Development of an Objective Space Suit Mobility Performance Metric Using Metabolic Cost and Functional Tasks

Existing methods for evaluating EVA suit performance and mobility have historically concentrated on isolated joint range of motion and torque. However, these techniques do little to evaluate how well a suited crewmember can actually perform during an EVA. An alternative method of characterizing suited mobility through measurement of metabolic cost to the wearer has been evaluated at Johnson Space Center over the past several years. The most recent study involved six test subjects completing multiple trials of various functional tasks in each of three different space suits; the results indicated it was often possible to discern between different suit designs on the basis of metabolic cost alone. However, other variables may have an effect on real-world suited performance; namely, completion time of the task, the gravity field in which the task is completed, etc. While previous results have analyzed completion time, metabolic cost, and metabolic cost normalized to system mass individually, it is desirable to develop a single metric comprising these (and potentially other) performance metrics. This paper outlines the background upon which this single-score metric is determined to be feasible, and initial efforts to develop such a metric. Forward work includes variable coefficient determination and verification of the metric through repeated testing.

McFarland, Shane M.↗
NASA NTRS2015

Supervised Remote Robot with Guided Autonomy and Teleoperation (SURROGATE): A Framework for Whole-Body Manipulation

The use of the cognitive capabilties of humans to help guide the autonomy of robotics platforms in what is typically called "supervised-autonomy" is becoming more commonplace in robotics research. The work discussed in this paper presents an approach to a human-in-the-loop mode of robot operation that integrates high level human cognition and commanding with the intelligence and processing power of autonomous systems. Our framework for a "Supervised Remote Robot with Guided Autonomy and Teleoperation" (SURROGATE) is demonstrated on a robotic platform consisting of a pan-tilt perception head, two 7-DOF arms connected by a single 7-DOF torso, mounted on a tracked-wheel base. We present an architecture that allows high-level supervisory commands and intents to be specified by a user that are then interpreted by the robotic system to perform whole body manipulation tasks autonomously. We use a concept of "behaviors" to chain together sequences of "actions" for the robot to perform which is then executed real time.

Hebert, Paul↗
NASA NTRS2019

High-Performance Spaceflight Computing (HPSC) Middleware Overview

High Performance Spacecraft Computing (HPSC) is a joint project between the National Aeronautics and Space Administration (NASA) and Air Force Research Lab (AFRL) to develop a high-performance multi-core radiation hardened flight processor. HPSC offers a new flight computing architecture to meet the needs of NASA missions through 2030 and beyond. Providing on the order of 100X the computational capacity of current flight processors for the same amount of power, the multicore architecture of the HPSC processor, or "Chiplet" provides unprecedented flexibility in a flight computing system by enabling the operating point to be set dynamically, trading among needs for computational performance, energy management and fault tolerance. The HPSC Chiplet is being developed by Boeing under contract to NASA, and is expected to provide prototypes in 2021. The HPSC Chiplet prototypes will be delivered with an evaluation board, system emulators, comprehensive system software, and a software development kit. In addition to the vendor deliverables, the AFRL is funding the development of a flexible Middleware to be developed by NASA Jet Propulsion Laboratory and NASA Goddard Space Flight Center. The HPSC Middleware provides a suite of thirteen high level services to manage the compute, memory and I/O resources of this complex device.This presentation will provide an overview of the HPSC project, including a hardware overview, system software overview, Middleware overview, and mission use cases. The hardware overview will provide a look at the 8 core High Performance Processing Subsystem (HPPS), the Real Time Processing Subsystem (RTPS), the Chiplet Configuration Management Subsystem, on chip peripherals, and high speed I/O. The system software overview will introduce the boot loaders, operating systems, device drivers, and software development environment. The Middleware overview will provide insight into the high-level services that will be provided to help mission developers manage the many resources and configurations made possible with the Chiplet. Finally, the presentation will provide a brief look at the mission use cases that can be enabled with this next generation architecture.

middleware↗
NASA NTRSDate not supplied

4BCO2 Model Validation and Comparisons Between Simulation and Ground and ISS Telemetry Data

The 4-bed CO2Scrubber (4BCO2), used to remove carbon dioxide from the atmosphere of the International Space Station(ISS), was built to improve upon its predecessors to meet the air revitalization needs of future missions. During this internship session, a Matlab wrapper of COMSOL models used to simulate the operation of the 4BCO2 unit was improved upon and validated against both ground test data and flight test data from the currently operating 4BCO2 unit. This presentation details the results of the activities and exercises performed during this internship session, such as the discussion on the plots generated from least squares minimization of the simulation linear driving force and sorbent bed dispersion correction factor multiplier. Plots derived from the collection and processing of telemetry data from the 4BCO2 unit aboard the ISS are discussed in relation to the work processing work performed, and comparisons between real-time CO2 removal rates and simulation removal rates are also discussed.

Larissa Lagria↗
DOE OSTI2020

Large-Scale Adoption of Self-Synchronized Universal Droop Controller-Based Inverters to Enable Ancillary Services for Different Modes of Distribution System Operations

This work proposes the large-scale adoption of self-synchronized universal droop controller (SUDC)-based inverters to enable ancillary services for different modes of distribution system operations. The IEEE 123 bus system was modeled on a real-time simulator to study the performance of large-scale adoption of SUDC inverters in a distribution system. The resulting data collected shows that the voltage and the frequency were regulated within ranges, such as less than 5% for voltage and less than 0.5% for frequency, under different load variations and grid operations. Also, the black start was achieved within 0.4 s without any voltage overshoot. Through the simulation and validation on a small microgrid and the IEEE 123 bus distribution system, it can be concluded that the SUDC was successfully adopted to regulate the voltage and the frequency within the given ranges, and black start achieved within 1 s without voltage overshoot for different modes of distribution system operations.

14 SOLAR ENERGY↗
DOE OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗
NASA NTRS1999

Real-Time Simulation of the X-33 Aerospace Engine

This paper discusses the development and performance of the X-33 Aerospike Engine RealTime Model. This model was developed for the purposes of control law development, six degree-of-freedom trajectory analysis, vehicle system integration testing, and hardware-in-the loop controller verification. The Real-Time Model uses time-step marching solution of non-linear differential equations representing the physical processes involved in the operation of a liquid propellant rocket engine, albeit in a simplified form. These processes include heat transfer, fluid dynamics, combustion, and turbomachine performance. Two engine models are typically employed in order to accurately model maneuvering and the powerpack-out condition where the power section of one engine is used to supply propellants to both engines if one engine malfunctions. The X-33 Real-Time Model is compared to actual hot fire test data and is been found to be in good agreement.

Aguilar, Robert↗
NASA NTRS2004

Verification and Validation Methodology of Real-Time Adaptive Neural Networks for Aerospace Applications

Recent research has shown that adaptive neural based control systems are very effective in restoring stability and control of an aircraft in the presence of damage or failures. The application of an adaptive neural network with a flight critical control system requires a thorough and proven process to ensure safe and proper flight operation. Unique testing tools have been developed as part of a process to perform verification and validation (V&V) of real time adaptive neural networks used in recent adaptive flight control system, to evaluate the performance of the on line trained neural networks. The tools will help in certification from FAA and will help in the successful deployment of neural network based adaptive controllers in safety-critical applications. The process to perform verification and validation is evaluated against a typical neural adaptive controller and the results are discussed.

Gupta, Pramod↗
DOE OSTI2023

Real-time estimation of the electron temperature profile in DIII-D by leveraging neural-network surrogate models

Control of both the magnitude and the shape of tokamak profiles will be necessary to achieve stable, high-performance plasmas. In order to reject disturbances in real time, feedback-control algorithms rely on accurate real-time knowledge of the plasma state. When diagnostics alone are insufficient, either because they are limited in number or their measurements are too noisy, observers can be used to combine diagnostic data with a response model to provide a better estimation of different plasma properties. An observer has been developed to estimate the electron temperature profile in real time using both diagnostic data from the Thomson scattering system and a model based on the electron heat transport equation describing the evolution of the electron temperature profile. Here, neural network surrogate models are leveraged to help improve the overall model prediction while staying within computation time constraints for real-time use. The observer algorithm is shown in offline tests to produce smooth profiles that are consistent with both the diagnostic data and the electron heat transport equation. When implemented into the real-time plasma control system, this observer will provide valuable information on the electron temperature profile to many potential feedback-control applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2017

Interactive Exploration Robots: Human-Robotic Collaboration and Interactions

For decades, NASA has employed different operational approaches for human and robotic missions. Human spaceflight missions to the Moon and in low Earth orbit have relied upon near-continuous communication with minimal time delays. During these missions, astronauts and mission control communicate interactively to perform tasks and resolve problems in real-time. In contrast, deep-space robotic missions are designed for operations in the presence of significant communication delay - from tens of minutes to hours. Consequently, robotic missions typically employ meticulously scripted and validated command sequences that are intermittently uplinked to the robot for independent execution over long periods. Over the next few years, however, we will see increasing use of robots that blend these two operational approaches. These interactive exploration robots will be remotely operated by humans on Earth or from a spacecraft. These robots will be used to support astronauts on the International Space Station (ISS), to conduct new missions to the Moon, and potentially to enable remote exploration of planetary surfaces in real-time. In this talk, I will discuss the technical challenges associated with building and operating robots in this manner, along with lessons learned from research conducted with the ISS and in the field.

Fong, Terry↗
NASA NTRS2006

Real-Time Diagnosis of Faults Using a Bank of Kalman Filters

A new robust method of automated real-time diagnosis of faults in an aircraft engine or a similar complex system involves the use of a bank of Kalman filters. In order to be highly reliable, a diagnostic system must be designed to account for the numerous failure conditions that an aircraft engine may encounter in operation. The method achieves this objective though the utilization of multiple Kalman filters, each of which is uniquely designed based on a specific failure hypothesis. A fault-detection-and-isolation (FDI) system, developed based on this method, is able to isolate faults in sensors and actuators while detecting component faults (abrupt degradation in engine component performance). By affording a capability for real-time identification of minor faults before they grow into major ones, the method promises to enhance safety and reduce operating costs. The robustness of this method is further enhanced by incorporating information regarding the aging condition of an engine. In general, real-time fault diagnostic methods use the nominal performance of a "healthy" new engine as a reference condition in the diagnostic process. Such an approach does not account for gradual changes in performance associated with aging of an otherwise healthy engine. By incorporating information on gradual, aging-related changes, the new method makes it possible to retain at least some of the sensitivity and accuracy needed to detect incipient faults while preventing false alarms that could result from erroneous interpretation of symptoms of aging as symptoms of failures. The figure schematically depicts an FDI system according to the new method. The FDI system is integrated with an engine, from which it accepts two sets of input signals: sensor readings and actuator commands. Two main parts of the FDI system are a bank of Kalman filters and a subsystem that implements FDI decision rules. Each Kalman filter is designed to detect a specific sensor or actuator fault. When a sensor or actuator fault occurs, large estimation errors are generated by all filters except the one using the correct hypothesis. By monitoring the residual output of each filter, the specific fault that has occurred can be detected and isolated on the basis of the decision rules. A set of parameters that indicate the performance of the engine components is estimated by the "correct" Kalman filter for use in detecting component faults. To reduce the loss of diagnostic accuracy and sensitivity in the face of aging, the FDI system accepts information from a steady-state-condition-monitoring system. This information is used to update the Kalman filters and a data bank of trim values representative of the current aging condition.

Kobayashi, Takahisa↗