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All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Integrated System Health Management (ISHM) for Test Stand and J-2X Engine: Core Implementation

ISHM capability enables a system to detect anomalies, determine causes and effects, predict future anomalies, and provides an integrated awareness of the health of the system to users (operators, customers, management, etc.). NASA Stennis Space Center, NASA Ames Research Center, and Pratt & Whitney Rocketdyne have implemented a core ISHM capability that encompasses the A1 Test Stand and the J-2X Engine. The implementation incorporates all aspects of ISHM; from anomaly detection (e.g. leaks) to root-cause-analysis based on failure mode and effects analysis (FMEA), to a user interface for an integrated visualization of the health of the system (Test Stand and Engine). The implementation provides a low functional capability level (FCL) in that it is populated with few algorithms and approaches for anomaly detection, and root-cause trees from a limited FMEA effort. However, it is a demonstration of a credible ISHM capability, and it is inherently designed for continuous and systematic augmentation of the capability. The ISHM capability is grounded on an integrating software environment used to create an ISHM model of the system. The ISHM model follows an object-oriented approach: includes all elements of the system (from schematics) and provides for compartmentalized storage of information associated with each element. For instance, a sensor object contains a transducer electronic data sheet (TEDS) with information that might be used by algorithms and approaches for anomaly detection, diagnostics, etc. Similarly, a component, such as a tank, contains a Component Electronic Data Sheet (CEDS). Each element also includes a Health Electronic Data Sheet (HEDS) that contains health-related information such as anomalies and health state. Some practical aspects of the implementation include: (1) near real-time data flow from the test stand data acquisition system through the ISHM model, for near real-time detection of anomalies and diagnostics, (2) insertion of the J-2X predictive model providing predicted sensor values for comparison with measured values and use in anomaly detection and diagnostics, and (3) insertion of third-party anomaly detection algorithms into the integrated ISHM model.

Figueroa, Jorge F.↗
NASA NTRS2013

Software Tools to Support the Assessment of System Health

This presentation provides an overview of three software tools that were developed by the NASA Glenn Research Center to support the assessment of system health: the Propulsion Diagnostic Method Evaluation Strategy (ProDIMES), the Systematic Sensor Selection Strategy (S4), and the Extended Testability Analysis (ETA) tool. Originally developed to support specific NASA projects in aeronautics and space, these software tools are currently available to U.S. citizens through the NASA Glenn Software Catalog. The ProDiMES software tool was developed to support a uniform comparison of propulsion gas path diagnostic methods. Methods published in the open literature are typically applied to dissimilar platforms with different levels of complexity. They often address different diagnostic problems and use inconsistent metrics for evaluating performance. As a result, it is difficult to perform a one ]to ]one comparison of the various diagnostic methods. ProDIMES solves this problem by serving as a theme problem to aid in propulsion gas path diagnostic technology development and evaluation. The overall goal is to provide a tool that will serve as an industry standard, and will truly facilitate the development and evaluation of significant Engine Health Management (EHM) capabilities. ProDiMES has been developed under a collaborative project of The Technical Cooperation Program (TTCP) based on feedback provided by individuals within the aircraft engine health management community. The S4 software tool provides a framework that supports the optimal selection of sensors for health management assessments. S4 is structured to accommodate user ]defined applications, diagnostic systems, search techniques, and system requirements/constraints. One or more sensor suites that maximize this performance while meeting other user ]defined system requirements that are presumed to exist. S4 provides a systematic approach for evaluating combinations of sensors to determine the set or sets of sensors that optimally meet the performance goals and the constraints. It identifies optimal sensor suite solutions by utilizing a merit (i.e., cost) function with one of several available optimization approaches. As part of its analysis, S4 can expose fault conditions that are difficult to diagnose due to an incomplete diagnostic philosophy and/or a lack of sensors. S4 was originally developed and applied to liquid rocket engines. It was subsequently used to study the optimized selection of sensors for a simulation ]based aircraft engine diagnostic system. The ETA Tool is a software ]based analysis tool that augments the testability analysis and reporting capabilities of a commercial ]off ]the ]shelf (COTS) package. An initial diagnostic assessment is performed by the COTS software using a user ]developed, qualitative, directed ]graph model of the system being analyzed. The ETA Tool accesses system design information captured within the model and the associated testability analysis output to create a series of six reports for various system engineering needs. These reports are highlighted in the presentation. The ETA Tool was developed by NASA to support the verification of fault management requirements early in the Launch Vehicle process. Due to their early development during the design process, the TEAMS ]based diagnostic model and the ETA Tool were able to positively influence the system design by highlighting gaps in failure detection, fault isolation, and failure recovery.

Melcher, Kevin J.↗
NASA NTRS1995

NASA Tech Briefs, November 1995

The contents include: 1) Mission Accomplished; 2) Resource Report: Marshall Space Flight Center; 3) NASA 1995 Software of the Year Award; 4) Microbolometers Based on Epitaxial YBa2Cu3O(sub 7-x) Thin Films; 5) Garnet Random-Access Memory; 6) Fabrication of SNS Weak Links on SOS Substrates; 7) High-Voltage MOSFET Switching Circuit; 8) Asymmetric Switching for a PWM H-Bridge Power Circuit; 9) Better Ohmic Contacts for InP Semiconductor Devices; 10) Low-Bandgap Thermovoltaic Materials and Devices; 11) Digital Frequency-Differencing Circuit; 12) Imaging Magnetometer; 13) Computer-Assisted Monitoring of a Complex System; 14) Buffered Telemetry Demodulator; 15) Compact Multifunction Inspection Head; 16) Optical Detection of Fractures in Ceramic Diaphragms; 17) Eddy-Current Detection of Cracks in Reinforced Carbon/Carbon; 18) Apparent Thermal Conductivity of Multilayer Insulation; 19) Optimizing Misch-Metal Compositions in Metal Hydride Anodes; 20) Device for Sampling Surface Contamination; 21) Probabilistic Failure Assessment for Fatigue; 22) Probabilistic Fatigue and Flaw-Propagation Analysis; 23) Windows Program for Driving the TDU-850 Printer; 24) Subband/Transform MATLAB Functions for Processing Images; 25) Computing Equilibrium Chemical Compositions; 26) Program Processes Thermocouple Readings; 27) ICAN-Second-Generation Integrated Composite Analyzer; 28) Integrated Composite Analyzer with Damping Capabilities; 29) Computing Efficiency of Transfer of Microwave Power; 30) Program Calculates Power Demands of Electronic Designs; 31) Cost-Estimation Program; 32) Program Estimates Areas Required by Electronic Designs; 33) Program to Balance Mapped Turbopump Assemblies; 34) BiblioTech; 35) Controlling Mirror Tilt With a Bimorph Actuator; 36) Burst-Disk Device Simulates Effect of Pyrotechnic Device; 37) Bearing-Mounting Concept Accommodates Thermal Expansion; 38) Parallel-Plate Acoustic Absorbers for Hot Environments; 39) Adjustable-Length Strut Withstands Large Cyclic Loads; 40) Tool Indicates Contact Angles in Bearing Raceways; 41) Gravity Slides With Magnetic Braking; 42) High-Torque, Lightweight, Pneumatically Driven Wrench for Small Spaces; 43) Device for Testing Compatibility of an O-Ring; 44) Magnetic Heat Pump Containing Flow Diverters; 45) Variable-Tilt Helicopter Rotor Mast; 46) "Beach-Ball" Robotic Rovers; 47) Apparatus Would Measure Temperatures of Ball Bearings; 48) Flexible Borescope for Inspecting Ducts; 49) Texturing Copper To Reduce Secondary Emission of Electrons; 50) Automated Laser Cutting in Three Dimensions; 51) Algorithm Helps Monitor Engine Operation; 52) Flexible Revision of Data-Processing Communications; 53) Software for Managing the Use of Land; 54) Thermal Strap Increases Cryocooling Efficiency; 55) Reversible Nut With Engagement Indication; 56) Control Algorithms for Kinematically Redundant Manipulators; 57) Computed Hydrogen-Flow Splits in a Rocket Engine; 58) Pressure and Thermal Modeling of Rocket Launches; 59) Field of View of a Spacecraft Antenna: Analysis and Software; 60) Digital Controller for Laser-Beam-Steering Subsystem; 61) More About Beam-Steering Subsystem for Laser Communication; 62) Digital Controller for Laser-Beam-Steering Subsystem: Part 2; 63) Interface Circuit Board for Space-Shuttle Communications; 64) Automated Planning of Spacecraft Telecommunications; 65) Artifacts of Spectral Analysis of Instrument Readings; 66) Neural-Network Controller for Vibration Suppression; 67) Adaptive Finite-Element Computation in Fracture Mechanics; 68) Attitude Control for the Cassini Spacecraft; 69) Analytical Model for Fluid Dynamics in a Microgravity Environment; 70) Study of Rocket-Engine Joints Bonded by NVCU/NARloy-Z; 71) Improved Silicon Nitride for Advanced Heat Engines; 72) Parameters for Welding Aluminum/Lithium Alloys; 73) Lightweight Composite Intertank Structure; 74) Foil Patches Seal Small Vacuum Leaks; 75) Data Base on Cables and Connectors; 76) Effect of Clock Mode on Radiation Hardnessf an ADC; and 77) Fault-Tolerant Control for a Robotic Inspection System.

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NASA NTRS2012

In-Situ Nondestructive Evaluation of Kevlar(Registered Trademark)and Carbon Fiber Reinforced Composite Micromechanics for Improved Composite Overwrapped Pressure Vessel Health Monitoring

NASA has been faced with recertification and life extension issues for epoxy-impregnated Kevlar 49 (K/Ep) and carbon (C/Ep) composite overwrapped pressure vessels (COPVs) used in various systems on the Space Shuttle and International Space Station, respectively. Each COPV has varying criticality, damage and repair histories, time at pressure, and pressure cycles. COPVs are of particular concern due to the insidious and catastrophic burst-before-leak failure mode caused by stress rupture (SR) of the composite overwrap. SR life has been defined [1] as the minimum time during which the composite maintains structural integrity considering the combined effects of stress level(s), time at stress level(s), and associated environment. SR has none of the features of predictability associated with metal pressure vessels, such as crack geometry, growth rate and size, or other features that lend themselves to nondestructive evaluation (NDE). In essence, the variability or surprise factor associated with SR cannot be eliminated. C/Ep COPVs are also susceptible to impact damage that can lead to reduced burst pressure even when the amount of damage to the COPV is below the visual detection threshold [2], thus necessitating implementation of a mechanical damage control plan [1]. Last, COPVs can also fail prematurely due to material or design noncompliance. In each case (SR, impact or noncompliance), out-of-family behavior is expected leading to a higher probability of failure at a given stress, hence, greater uncertainty in performance. For these reasons, NASA has been actively engaged in research to develop NDE methods that can be used during post-manufacture qualification, in-service inspection, and in-situ structural health monitoring. Acoustic emission (AE) is one of the more promising NDE techniques for detecting and monitoring, in real-time, the strain energy release and corresponding stress-wave propagation produced by actively growing flaws and defects in composite materials [3,4,5,6,7,8]. To gain further insight into the mechanisms responsible for composite rupture, broadband modal acoustic emission analysis was used. Also, since AE data reduction proved to be very time consuming, specialized data reduction software was written to automate the process.

Waller, Jess↗
NASA NTRS2009

Connecting Research and Practice: An Experience Report on Research Infusion with SAVE

NASA systems need to be highly dependable to avoid catastrophic mission failures. This calls for rigorous engineering processes including meticulous validation and verification. However, NASA systems are often highly distributed and overwhelmingly complex, making the software portion of these systems challenging to understand, maintain, change, reuse, and test. NASA's systems are long-lived and the software maintenance process typically constitutes 60-80% of the total cost of the entire lifecycle. Thus, in addition to the technical challenges of ensuring high life-time quality of NASA's systems, the post-development phase also presents a significant financial burden. Some of NASA's software-related challenges could potentially be addressed by some of the many powerful technologies that are being developed in software research laboratories. Many of these research technologies seek to facilitate maintenance and evolution by for example architecting, designing and modeling for quality, flexibility, and reuse. Other technologies attempt to detect and remove defects and other quality issues by various forms of automated defect detection, architecture analysis, and various forms of sophisticated simulation and testing. However promising, most such research technologies nevertheless do not make the transition from the research lab to the software lab. One reason the transition from research to practice seldom occurs is that research infusion and technology transfer is difficult. For example, factors related to the technology are sometimes overshadowed by other types of factors such as reluctance to change and therefore prohibits the technology from sticking. Successful infusion might also take very long time. One famous study showed that the discrepancy between the conception of the idea and its practical use was 18 years plus or minus three. Nevertheless, infusing new technology is possible. We have found that it takes special circumstances for such research infusion to succeed: 1) there must be evidence that the technology works in the practitioner's particular domain, 2) there must be a potential for great improvements and enhanced competitive edge for the practitioner, 3) the practitioner has to have strong individual curiosity and continuous interest in trying out new technologies, 4) the practitioner has to have support on multiple levels (i.e. from the researchers, from management, from sponsors etc), and 5) to remain infused, the new technology has to be integrated into the practitioner's processes so that it becomes a natural part of the daily work. NASA IV&V's Research Infusion initiative sponsored by NASA's Office of Safety & Mission Assurance (OSMA) through the Software Assurance Research Program (SARP), strives to overcome some of the problems related to research infusion.

Lindvall, Mikael↗
DOE OSTI2025

Quantitative insights for diagnosing performance bottlenecks in lithium–sulfur batteries

Lithium–sulfur (Li–S) batteries hold significant promise for electric vehicles and aviation due to their high energy density and cost-effectiveness. However, understanding the root causes of performance degradation remains a formidable challenge, as the interplay of multiple factors obscures key failure mechanisms. A major limitation has been the inability to quantify soluble sulfur species within practical detection limits accurately and to correlate electrochemical processes with associated physical inventory changes. Here, we introduce the high-performance liquid chromatography-ultraviolet spectroscopy and gas chromatography sequential characterization (HUGS) toolkit, capable of precisely quantifying seven distinct sulfur and polysulfide species at concentrations as low as 40 ppb. HUGS has been successfully applied to practical coin and pouch cells without requiring cell modification. Furthermore, our self-developed software, Dr HUGS, enhanced the data analysis speed by over 30 times, enabling multi-source data integration and delivering comprehensive analysis results within minutes. Using HUGS, we identify significant capacity losses from inactive lithium and sulfur during initial cycles and sulfide-rich solid–electrolyte interphase (SEI) formation on the anode during later cycles. Notably, our findings reveal that soluble polysulfides have minimal contributions to capacity loss, challenging long-standing assumptions. Moreover, HUGS demonstrates that constant-pressure setups in Li–S pouch cells improve compositional uniformity compared to constant-gap configurations. For sulfurized polyacrylonitrile (SPAN) cathodes, unique issues such as non-sulfide SEI formation and lithium pulverization are observed, which can be mitigated through localized high-concentration electrolytes to enhance lithium inventory retention. By enabling precise quantification of critical inventory components, HUGS provides transformative insights into failure mechanisms across various electrolytes and cathode chemistries, guiding rational design strategies for next-generation energy storage systems.

25 ENERGY STORAGE↗
NASA NTRS2004

Advanced Signal Conditioners for Data-Acquisition Systems

Signal conditioners embodying advanced concepts in analog and digital electronic circuitry and software have been developed for use in data-acquisition systems that are required to be compact and lightweight, to utilize electric energy efficiently, and to operate with high reliability, high accuracy, and high power efficiency, without intervention by human technicians. These signal conditioners were originally intended for use aboard spacecraft. There are also numerous potential terrestrial uses - especially in the fields of aeronautics and medicine, wherein it is necessary to monitor critical functions. Going beyond the usual analog and digital signal-processing functions of prior signal conditioners, the new signal conditioner performs the following additional functions: It continuously diagnoses its own electronic circuitry, so that it can detect failures and repair itself (as described below) within seconds. It continuously calibrates itself on the basis of a highly accurate and stable voltage reference, so that it can continue to generate accurate measurement data, even under extreme environmental conditions. It repairs itself in the sense that it contains a micro-controller that reroutes signals among redundant components as needed to maintain the ability to perform accurate and stable measurements. It detects deterioration of components, predicts future failures, and/or detects imminent failures by means of a real-time analysis in which, among other things, data on its present state are continuously compared with locally stored historical data. It minimizes unnecessary consumption of electric energy. The design architecture divides the signal conditioner into three main sections: an analog signal section, a digital module, and a power-management section. The design of the analog signal section does not follow the traditional approach of ensuring reliability through total redundancy of hardware: Instead, following an approach called spare parts tool box, the reliability of each component is assessed in terms of such considerations as risks of damage, mean times between failures, and the effects of certain failures on the performance of the signal conditioner as a whole system. Then, fewer or more spares are assigned for each affected component, pursuant to the results of this analysis, in order to obtain the required degree of reliability of the signal conditioner as a whole system. The digital module comprises one or more processors and field-programmable gate arrays, the number of each depending on the results of the aforementioned analysis. The digital module provides redundant control, monitoring, and processing of several analog signals. It is designed to minimize unnecessary consumption of electric energy, including, when possible, going into a low-power "sleep" mode that is implemented in firmware. The digital module communicates with external equipment via a personal-computer serial port. The digital module monitors the "health" of the rest of the signal conditioner by processing defined measurements and/or trends. It automatically makes adjustments to respond to channel failures, compensate for effects of temperature, and maintain calibration.

Lucena, Angel↗
NASA NTRS1990

Improving system reliability through formal analysis and use of checks in software

Software is playing increasingly important roles in avionics systems. It is widely used in navigation and, in some cases, in control loops that maintain aircraft stability. To guarantee the safety of flight systems, the FAA requires that critical components have a probability of failure no greater than 10(exp -9) per hour of flight. Software is being used to diagnose system components for failure. SIFT (Software Implemented Fault Tolerance) was a computer system developed to study the use of software to check for failure and manage processor reconfiguration. To guarantee that software satisfies its specifications, formal verification can be used. With this a program and its specification are viewed as mathematical objects, and a mathematical proof is used to show that the program and its specification are equivalent. In previous research, a theory of checking was developed to offer assistance in analyzing specifications and designing run-time checks. In the theory, checking is considered abstractly in terms of n-ary relations much like those of relational database theory. Within the theory check are categorized, checks on input and checks on results are considered, and formal attention is given to the minimization and logical combination of checks. The focus is upon input checks and the obstacles in checking input to critical systems. A central concern is with a property referred to as independence. The concern is with circumstances under which it is possible to apply isolated, independent checks to separate sensor inputs and be assure that all illegal input will be properly detected. Presently, independence is being investigated and checked in the context of the GCS (Guidance and Control System). The GCS simulator is intended for testing software that implements control laws for landing spacecraft. The large number of inputs and their complex interrelationships provide an exciting context in which to investigate independence and the difficulties of supplying input checks.

Staknis, Mark E.↗
NASA NTRS2015

A Vehicle Management End-to-End Testing and Analysis Platform for Validation of Mission and Fault Management Algorithms to Reduce Risk for NASA's Space Launch System

The development of the Space Launch System (SLS) launch vehicle requires cross discipline teams with extensive knowledge of launch vehicle subsystems, information theory, and autonomous algorithms dealing with all operations from pre-launch through on orbit operations. The characteristics of these systems must be matched with the autonomous algorithm monitoring and mitigation capabilities for accurate control and response to abnormal conditions throughout all vehicle mission flight phases, including precipitating safing actions and crew aborts. This presents a large complex systems engineering challenge being addressed in part by focusing on the specific subsystems handling of off-nominal mission and fault tolerance. Using traditional model based system and software engineering design principles from the Unified Modeling Language (UML), the Mission and Fault Management (M&FM) algorithms are crafted and vetted in specialized Integrated Development Teams composed of multiple development disciplines. NASA also has formed an M&FM team for addressing fault management early in the development lifecycle. This team has developed a dedicated Vehicle Management End-to-End Testbed (VMET) that integrates specific M&FM algorithms, specialized nominal and off-nominal test cases, and vendor-supplied physics-based launch vehicle subsystem models. The flexibility of VMET enables thorough testing of the M&FM algorithms by providing configurable suites of both nominal and off-nominal test cases to validate the algorithms utilizing actual subsystem models. The intent is to validate the algorithms and substantiate them with performance baselines for each of the vehicle subsystems in an independent platform exterior to flight software test processes. In any software development process there is inherent risk in the interpretation and implementation of concepts into software through requirements and test processes. Risk reduction is addressed by working with other organizations such as S&MA, Structures and Environments, GNC, Orion, the Crew Office, Flight Operations, and Ground Operations by assessing performance of the M&FM algorithms in terms of their ability to reduce Loss of Mission and Loss of Crew probabilities. In addition, through state machine and diagnostic modeling, analysis efforts investigate a broader suite of failure effects and detection and responses that can be tested in VMET and confirm that responses do not create additional risks or cause undesired states through interactive dynamic effects with other algorithms and systems. VMET further contributes to risk reduction by prototyping and exercising the M&FM algorithms early in their implementation and without any inherent hindrances such as meeting FSW processor scheduling constraints due to their target platform - ARINC 653 partitioned OS, resource limitations, and other factors related to integration with other subsystems not directly involved with M&FM. The plan for VMET encompasses testing the original M&FM algorithms coded in the same C++ language and state machine architectural concepts as that used by Flight Software. This enables the development of performance standards and test cases to characterize the M&FM algorithms and sets a benchmark from which to measure the effectiveness of M&FM algorithms performance in the FSW development and test processes. This paper is outlined in a systematic fashion analogous to a lifecycle process flow for engineering development of algorithms into software and testing. Section I describes the NASA SLS M&FM context, presenting the current infrastructure, leading principles, methods, and participants. Section II defines the testing philosophy of the M&FM algorithms as related to VMET followed by section III, which presents the modeling methods of the algorithms to be tested and validated in VMET. Its details are then further presented in section IV followed by Section V presenting integration, test status, and state analysis. Finally, section VI addresses the summary and forward directions followed by the appendices presenting relevant information on terminology and documentation.

Trevino, Luis↗
NASA NTRS2002

The Use of Probabilistic Methods to Evaluate the Systems Impact of Component Design Improvements on Large Turbofan Engines

Probabilistic Structural Analysis (PSA) is now commonly used for predicting the distribution of time/cycles to failure of turbine blades and other engine components. These distributions are typically based on fatigue/fracture and creep failure modes of these components. Additionally, reliability analysis is used for taking test data related to particular failure modes and calculating failure rate distributions of electronic and electromechanical components. How can these individual failure time distributions of structural, electronic and electromechanical component failure modes be effectively combined into a top level model for overall system evaluation of component upgrades, changes in maintenance intervals, or line replaceable unit (LRU) redesign? This paper shows an example of how various probabilistic failure predictions for turbine engine components can be evaluated and combined to show their effect on overall engine performance. A generic model of a turbofan engine was modeled using various Probabilistic Risk Assessment (PRA) tools (Quantitative Risk Assessment Software (QRAS) etc.). Hypothetical PSA results for a number of structural components along with mitigation factors that would restrict the failure mode from propagating to a Loss of Mission (LOM) failure were used in the models. The output of this program includes an overall failure distribution for LOM of the system. The rank and contribution to the overall Mission Success (MS) is also given for each failure mode and each subsystem. This application methodology demonstrates the effectiveness of PRA for assessing the performance of large turbine engines. Additionally, the effects of system changes and upgrades, the application of different maintenance intervals, inclusion of new sensor detection of faults and other upgrades were evaluated in determining overall turbine engine reliability.

Packard, Michael H.↗
NASA NTRS2008

Inductive System Monitors Tasks

The Inductive Monitoring System (IMS) software developed at Ames Research Center uses artificial intelligence and data mining techniques to build system-monitoring knowledge bases from archived or simulated sensor data. This information is then used to detect unusual or anomalous behavior that may indicate an impending system failure. Currently helping analyze data from systems that help fly and maintain the space shuttle and the International Space Station (ISS), the IMS has also been employed by data classes are then used to build a monitoring knowledge base. In real time, IMS performs monitoring functions: determining and displaying the degree of deviation from nominal performance. IMS trend analyses can detect conditions that may indicate a failure or required system maintenance. The development of IMS was motivated by the difficulty of producing detailed diagnostic models of some system components due to complexity or unavailability of design information. Successful applications have ranged from real-time monitoring of aircraft engine and control systems to anomaly detection in space shuttle and ISS data. IMS was used on shuttle missions STS-121, STS-115, and STS-116 to search the Wing Leading Edge Impact Detection System (WLEIDS) data for signs of possible damaging impacts during launch. It independently verified findings of the WLEIDS Mission Evaluation Room (MER) analysts and indicated additional points of interest that were subsequently investigated by the MER team. In support of the Exploration Systems Mission Directorate, IMS is being deployed as an anomaly detection tool on ISS mission control consoles in the Johnson Space Center Mission Operations Directorate. IMS has been trained to detect faults in the ISS Control Moment Gyroscope (CMG) systems. In laboratory tests, it has already detected several minor anomalies in real-time CMG data. When tested on archived data, IMS was able to detect precursors of the CMG1 failure nearly 15 hours in advance of the actual failure event. In the Aeronautics Research Mission Directorate, IMS successfully performed real-time engine health analysis. IMS was able to detect simulated failures and actual engine anomalies in an F/A-18 aircraft during the course of 25 test flights. IMS is also being used in colla

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NASA NTRS2013

Progress of a Cross-correlation Based Optical Strain Measurement Technique for Detecting Radial Growth on a Rotating Disk

The Aeronautical Sciences Project under NASAs Fundamental Aeronautics Program is extremely interested in the development of fault detection technologies, such as optical surface measurements in the internal parts of a flow path, for in situ health monitoring of gas turbine engines. In situ health monitoring has the potential to detect flaws, i.e. cracks in key components, such as engine turbine disks, before the flaws lead to catastrophic failure. In the present study, a cross-correlation imaging technique is investigated in a proof-of-concept study as a possible optical technique to measure the radial growth and strain field on an already cracked sub-scale turbine engine disk under loaded conditions in the NASA Glenn Research Centers High Precision Rotordynamics Laboratory. The optical strain measurement technique under investigation offers potential fault detection using an applied background consisting of a high-contrast random speckle pattern and imaging the background under unloaded and loaded conditions with a CCD camera. Spinning the cracked disk at high speeds induces an external load, resulting in a radial growth of the disk of approximately 50.8-m in the flawed region and hence, a localized strain field. When imaging the cracked disk under static conditions, the disk will appear shifted. The resulting background displacements between the two images will then be measured using the two-dimensional cross-correlation algorithms implemented in standard Particle Image Velocimetry (PIV) software to track the disk growth, which facilitates calculation of the localized strain field. In order to develop and validate this optical strain measurement technique an initial proof-of-concept experiment is carried out in a controlled environment. Using PIV optimization principles and guidelines, three potential backgrounds, for future use on the rotating disk, are developed and investigated in the controlled experiment. A range of known shifts are induced on the backgrounds; reference and data images are acquired before and after the induced shift, respectively, and the images are processed using the cross- correlation algorithms in order to determine the background displacements. The effectiveness of each background at resolving the known shift is evaluated and discussed in order to choose to the most suitable background to be implemented onto a rotating disk in the Rotordynamics Lab. Although testing on the rotating disk has not yet been performed, the driving principles behind the development of the present optical technique are based upon critical aspects of the future experiment, such as the amount of expected radial growth, disk analysis, and experimental design and are therefore addressed in the paper.

Clem, Michelle M.↗
NASA NTRS2013

Investigation of a Cross-Correlation Based Optical Strain Measurement Technique for Detecting radial Growth on a Rotating Disk

The Aeronautical Sciences Project under NASA`s Fundamental Aeronautics Program is extremely interested in the development of novel measurement technologies, such as optical surface measurements in the internal parts of a flow path, for in situ health monitoring of gas turbine engines. In situ health monitoring has the potential to detect flaws, i.e. cracks in key components, such as engine turbine disks, before the flaws lead to catastrophic failure. In the present study, a cross-correlation imaging technique is investigated in a proof-of-concept study as a possible optical technique to measure the radial growth and strain field on an already cracked sub-scale turbine engine disk under loaded conditions in the NASA Glenn Research Center`s High Precision Rotordynamics Laboratory. The optical strain measurement technique under investigation offers potential fault detection using an applied high-contrast random speckle pattern and imaging the pattern under unloaded and loaded conditions with a CCD camera. Spinning the cracked disk at high speeds induces an external load, resulting in a radial growth of the disk of approximately 50.0-im in the flawed region and hence, a localized strain field. When imaging the cracked disk under static conditions, the disk will be undistorted; however, during rotation the cracked region will grow radially, thus causing the applied particle pattern to be .shifted`. The resulting particle displacements between the two images will then be measured using the two-dimensional cross-correlation algorithms implemented in standard Particle Image Velocimetry (PIV) software to track the disk growth, which facilitates calculation of the localized strain field. In order to develop and validate this optical strain measurement technique an initial proof-of-concept experiment is carried out in a controlled environment. Using PIV optimization principles and guidelines, three potential speckle patterns, for future use on the rotating disk, are developed and investigated in the controlled experiment. A range of known shifts are induced on the patterns; reference and data images are acquired before and after the induced shift, respectively, and the images are processed using the cross-correlation algorithms in order to determine the particle displacements. The effectiveness of each pattern at resolving the known shift is evaluated and discussed in order to choose the most suitable pattern to be implemented onto a rotating disk in the Rotordynamics Lab. Although testing on the rotating disk has not yet been performed, the driving principles behind the development of the present optical technique are based upon critical aspects of the future experiment, such as the amount of expected radial growth, disk analysis, and experimental design and are therefore addressed in the paper.

Clem, Michelle M.↗
NASA NTRS2015

A Vehicle Management End-to-End Testing and Analysis Platform for Validation of Mission and Fault Management Algorithms to Reduce Risk for NASA's Space Launch System

The engineering development of the new Space Launch System (SLS) launch vehicle requires cross discipline teams with extensive knowledge of launch vehicle subsystems, information theory, and autonomous algorithms dealing with all operations from pre-launch through on orbit operations. The characteristics of these spacecraft systems must be matched with the autonomous algorithm monitoring and mitigation capabilities for accurate control and response to abnormal conditions throughout all vehicle mission flight phases, including precipitating safing actions and crew aborts. This presents a large and complex system engineering challenge, which is being addressed in part by focusing on the specific subsystems involved in the handling of off-nominal mission and fault tolerance with response management. Using traditional model based system and software engineering design principles from the Unified Modeling Language (UML) and Systems Modeling Language (SysML), the Mission and Fault Management (M&FM) algorithms for the vehicle are crafted and vetted in specialized Integrated Development Teams (IDTs) composed of multiple development disciplines such as Systems Engineering (SE), Flight Software (FSW), Safety and Mission Assurance (S&MA) and the major subsystems and vehicle elements such as Main Propulsion Systems (MPS), boosters, avionics, Guidance, Navigation, and Control (GNC), Thrust Vector Control (TVC), and liquid engines. These model based algorithms and their development lifecycle from inception through Flight Software certification are an important focus of this development effort to further insure reliable detection and response to off-nominal vehicle states during all phases of vehicle operation from pre-launch through end of flight. NASA formed a dedicated M&FM team for addressing fault management early in the development lifecycle for the SLS initiative. As part of the development of the M&FM capabilities, this team has developed a dedicated testbed that integrates specific M&FM algorithms, specialized nominal and off-nominal test cases, and vendor-supplied physics-based launch vehicle subsystem models. Additionally, the team has developed processes for implementing and validating these algorithms for concept validation and risk reduction for the SLS program. The flexibility of the Vehicle Management End-to-end Testbed (VMET) enables thorough testing of the M&FM algorithms by providing configurable suites of both nominal and off-nominal test cases to validate the developed algorithms utilizing actual subsystem models such as MPS. The intent of VMET is to validate the M&FM algorithms and substantiate them with performance baselines for each of the target vehicle subsystems in an independent platform exterior to the flight software development infrastructure and its related testing entities. In any software development process there is inherent risk in the interpretation and implementation of concepts into software through requirements and test cases into flight software compounded with potential human errors throughout the development lifecycle. Risk reduction is addressed by the M&FM analysis group working with other organizations such as S&MA, Structures and Environments, GNC, Orion, the Crew Office, Flight Operations, and Ground Operations by assessing performance of the M&FM algorithms in terms of their ability to reduce Loss of Mission and Loss of Crew probabilities. In addition, through state machine and diagnostic modeling, analysis efforts investigate a broader suite of failure effects and associated detection and responses that can be tested in VMET to ensure that failures can be detected, and confirm that responses do not create additional risks or cause undesired states through interactive dynamic effects with other algorithms and systems. VMET further contributes to risk reduction by prototyping and exercising the M&FM algorithms early in their implementation and without any inherent hindrances such as meeting FSW processor scheduling constraints due to their target platform - ARINC 653 partitioned OS, resource limitations, and other factors related to integration with other subsystems not directly involved with M&FM such as telemetry packing and processing. The baseline plan for use of VMET encompasses testing the original M&FM algorithms coded in the same C++ language and state machine architectural concepts as that used by Flight Software. This enables the development of performance standards and test cases to characterize the M&FM algorithms and sets a benchmark from which to measure the effectiveness of M&FM algorithms performance in the FSW development and test processes.

Trevino, Luis↗
NASA NTRS2015

A Vehicle Management End-to-End Testing and Analysis Platform for Validation of Mission and Fault Management Algorithms to Reduce Risk for NASAs Space Launch System

The engineering development of the National Aeronautics and Space Administration's (NASA) new Space Launch System (SLS) requires cross discipline teams with extensive knowledge of launch vehicle subsystems, information theory, and autonomous algorithms dealing with all operations from pre-launch through on orbit operations. The nominal and off-nominal characteristics of SLS's elements and subsystems must be understood and matched with the autonomous algorithm monitoring and mitigation capabilities for accurate control and response to abnormal conditions throughout all vehicle mission flight phases, including precipitating safing actions and crew aborts. This presents a large and complex systems engineering challenge, which is being addressed in part by focusing on the specific subsystems involved in the handling of off-nominal mission and fault tolerance with response management. Using traditional model-based system and software engineering design principles from the Unified Modeling Language (UML) and Systems Modeling Language (SysML), the Mission and Fault Management (M&FM) algorithms for the vehicle are crafted and vetted in Integrated Development Teams (IDTs) composed of multiple development disciplines such as Systems Engineering (SE), Flight Software (FSW), Safety and Mission Assurance (S&MA) and the major subsystems and vehicle elements such as Main Propulsion Systems (MPS), boosters, avionics, Guidance, Navigation, and Control (GNC), Thrust Vector Control (TVC), and liquid engines. These model-based algorithms and their development lifecycle from inception through FSW certification are an important focus of SLS's development effort to further ensure reliable detection and response to off-nominal vehicle states during all phases of vehicle operation from pre-launch through end of flight. To test and validate these M&FM algorithms a dedicated test-bed was developed for full Vehicle Management End-to-End Testing (VMET). For addressing fault management (FM) early in the development lifecycle for the SLS program, NASA formed the M&FM team as part of the Integrated Systems Health Management and Automation Branch under the Spacecraft Vehicle Systems Department at the Marshall Space Flight Center (MSFC). To support the development of the FM algorithms, the VMET developed by the M&FM team provides the ability to integrate the algorithms, perform test cases, and integrate vendor-supplied physics-based launch vehicle (LV) subsystem models. Additionally, the team has developed processes for implementing and validating the M&FM algorithms for concept validation and risk reduction. The flexibility of the VMET capabilities enables thorough testing of the M&FM algorithms by providing configurable suites of both nominal and off-nominal test cases to validate the developed algorithms utilizing actual subsystem models such as MPS, GNC, and others. One of the principal functions of VMET is to validate the M&FM algorithms and substantiate them with performance baselines for each of the target vehicle subsystems in an independent platform exterior to the flight software test and validation processes. In any software development process there is inherent risk in the interpretation and implementation of concepts from requirements and test cases into flight software compounded with potential human errors throughout the development and regression testing lifecycle. Risk reduction is addressed by the M&FM group but in particular by the Analysis Team working with other organizations such as S&MA, Structures and Environments, GNC, Orion, Crew Office, Flight Operations, and Ground Operations by assessing performance of the M&FM algorithms in terms of their ability to reduce Loss of Mission (LOM) and Loss of Crew (LOC) probabilities. In addition, through state machine and diagnostic modeling, analysis efforts investigate a broader suite of failure effects and associated detection and responses to be tested in VMET to ensure reliable failure detection, and confirm responses do not create additional risks or cause undesired states through interactive dynamic effects with other algorithms and systems. VMET further contributes to risk reduction by prototyping and exercising the M&FM algorithms early in their implementation and without any inherent hindrances such as meeting FSW processor scheduling constraints due to their target platform - the ARINC 6535-partitioned Operating System, resource limitations, and other factors related to integration with other subsystems not directly involved with M&FM such as telemetry packing and processing. The baseline plan for use of VMET encompasses testing the original M&FM algorithms coded in the same C++ language and state machine architectural concepts as that used by FSW. This enables the development of performance standards and test cases to characterize the M&FM algorithms and sets a benchmark from which to measure their effectiveness and performance in the exterior FSW development and test processes. This paper is outlined in a systematic fashion analogous to a lifecycle process flow for engineering development of algorithms into software and testing. Section I describes the NASA SLS M&FM context, presenting the current infrastructure, leading principles, methods, and participants. Section II defines the testing philosophy of the M&FM algorithms as related to VMET followed by section III, which presents the modeling methods of the algorithms to be tested and validated in VMET. Its details are then further presented in section IV followed by Section V presenting integration, test status, and state analysis. Finally, section VI addresses the summary and forward directions followed by the appendices presenting relevant information on terminology and documentation.

Trevino, Luis↗
NASA NTRS2001

Automating Trend Analysis for Spacecraft Constellations

Spacecraft trend analysis is a vital mission operations function performed by satellite controllers and engineers, who perform detailed analyses of engineering telemetry data to diagnose subsystem faults and to detect trends that may potentially lead to degraded subsystem performance or failure in the future. It is this latter function that is of greatest importance, for careful trending can often predict or detect events that may lead to a spacecraft's entry into safe-hold. Early prediction and detection of such events could result in the avoidance of, or rapid return to service from, spacecraft safing, which not only results in reduced recovery costs but also in a higher overall level of service for the satellite system. Contemporary spacecraft trending activities are manually intensive and are primarily performed diagnostically after a fault occurs, rather than proactively to predict its occurrence. They also tend to rely on information systems and software that are oudated when compared to current technologies. When coupled with the fact that flight operations teams often have limited resources, proactive trending opportunities are limited, and detailed trend analysis is often reserved for critical responses to safe holds or other on-orbit events such as maneuvers. While the contemporary trend analysis approach has sufficed for current single-spacecraft operations, it will be unfeasible for NASA's planned and proposed space science constellations. Missions such as the Dynamics, Reconnection and Configuration Observatory (DRACO), for example, are planning to launch as many as 100 'nanospacecraft' to form a homogenous constellation. A simple extrapolation of resources and manpower based on single-spacecraft operations suggests that trending for such a large spacecraft fleet will be unmanageable, unwieldy, and cost-prohibitive. It is therefore imperative that an approach to automating the spacecraft trend analysis function be studied, developed, and applied to missions such as DRACO with the intent that mission operations costs be significantly reduced. The goal of the Constellation Spacecraft Trend Analysis Toolkit (CSTAT) project is to serve as the pathfinder for a fully automated trending system to support spacecraft constellations. The development approach to be taken is evolutionary. In the first year of the project, the intent is to significantly advance the state of the art in current trending systems through improved functionality and increased automation. In the second year, the intent is to add an expert system shell, likely through the adaptation of an existing commercial-off-the-shelf (COTS) or government-off-the-shelf (GOTS) tool to implement some level of the trending intelligence that humans currently provide in manual operations. In the third year, the intent is to infuse the resulting technology into a near-term constellation or formation-flying mission to test it and gain experience in automated trending. The lessons learned from the real missions operations experience will then be used to improve the system, and to ultimately incorporate it into a fully autonomous, closed-loop mission operations system that is truly capable of supporting large constellations. In this paper, the process of automating trend analysis for spacecraft constellations will be addressed. First, the results of a survey on automation in spacecraft mission operations in general, and in trending systems in particular will be presented to provide an overview of the current state of the art. Next, a rule-based model for implementing intelligent spacecraft subsystem trending will be then presented, followed by a survey of existing COTS/GOTS tools that could be adapted for implementing such a model. The baseline design and architecture of the CSTAT system will be presented. Finally, some results obtained from initial software tests and demonstrations will be presented.

Davis, George↗