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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Integrated Systems Health Management for Intelligent Systems

The implementation of an integrated system health management (ISHM) capability is fundamentally linked to the management of data, information, and knowledge (DIaK) with the purposeful objective of determining the health of a system. It is akin to having a team of experts who are all individually and collectively observing and analyzing a complex system, and communicating effectively with each other in order to arrive at an accurate and reliable assessment of its health. In this paper, concepts, procedures, and approaches are presented as a foundation for implementing an intelligent systems ]relevant ISHM capability. The capability stresses integration of DIaK from all elements of a system. Both ground-based (remote) and on-board ISHM capabilities are compared and contrasted. The information presented is the result of many years of research, development, and maturation of technologies, and of prototype implementations in operational systems.

Figueroa, Fernando↗
NASA NTRS1992

Health management and controls for earth to orbit propulsion systems

Fault detection and isolation for advanced rocket engine controllers are discussed focusing on advanced sensing systems and software which significantly improve component failure detection for engine safety and health management. Aerojet's Space Transportation Main Engine controller for the National Launch System is the state of the art in fault tolerant engine avionics. Health management systems provide high levels of automated fault coverage and significantly improve vehicle delivered reliability and lower preflight operations costs. Key technologies, including the sensor data validation algorithms and flight capable spectrometers, have been demonstrated in ground applications and are found to be suitable for bridging programs into flight applications.

Bickford, R. L.↗
NASA NTRS1990

Analytical concepts for health management systems of liquid rocket engines

Substantial improvement in health management systems performance can be realized by implementing advanced analytical methods of processing existing liquid rocket engine sensor data. In this paper, such techniques ranging from time series analysis to multisensor pattern recognition to expert systems to fault isolation models are examined and contrasted. The performance of several of these methods is evaluated using data from test firings of the Space Shuttle main engines.

Williams, Richard↗
NASA NTRS1993

Intelligent neuroprocessors for in-situ launch vehicle propulsion systems health management

Efficacy of existing on-board propulsion systems health management systems (HMS) are severely impacted by computational limitations (e.g., low sampling rates); paradigmatic limitations (e.g., low-fidelity logic/parameter redlining only, false alarms due to noisy/corrupted sensor signatures, preprogrammed diagnostics only); and telemetry bandwidth limitations on space/ground interactions. Ultra-compact/light, adaptive neural networks with massively parallel, asynchronous, fast reconfigurable and fault-tolerant information processing properties have already demonstrated significant potential for inflight diagnostic analyses and resource allocation with reduced ground dependence. In particular, they can automatically exploit correlation effects across multiple sensor streams (plume analyzer, flow meters, vibration detectors, etc.) so as to detect anomaly signatures that cannot be determined from the exploitation of single sensor. Furthermore, neural networks have already demonstrated the potential for impacting real-time fault recovery in vehicle subsystems by adaptively regulating combustion mixture/power subsystems and optimizing resource utilization under degraded conditions. A class of high-performance neuroprocessors, developed at JPL, that have demonstrated potential for next-generation HMS for a family of space transportation vehicles envisioned for the next few decades, including HLLV, NLS, and space shuttle is presented. Of fundamental interest are intelligent neuroprocessors for real-time plume analysis, optimizing combustion mixture-ratio, and feedback to hydraulic, pneumatic control systems. This class includes concurrently asynchronous reprogrammable, nonvolatile, analog neural processors with high speed, high bandwidth electronic/optical I/O interfaced, with special emphasis on NASA's unique requirements in terms of performance, reliability, ultra-high density ultra-compactness, ultra-light weight devices, radiation hardened devices, power stringency, and long life terms.

Gulati, S.↗
NASA NTRS2004

Propulsion Integrated Vehicle Health Management Technology Experiment (PITEX) Conducted

The Propulsion Integrated Vehicle Health Management (IVHM) Technology Experiment (PITEX) is a continuing NASA effort being conducted cooperatively by the NASA Glenn Research Center, the NASA Ames Research Center, and the NASA Kennedy Space Center. It was a key element of a Space Launch Initiative risk-reduction task performed by the Northrop Grumman Corporation in El Segundo, California. PITEX's main objectives are the continued maturation of diagnostic technologies that are relevant to second generation reusable launch vehicle (RLV) subsystems and the assessment of the real-time performance of the PITEX diagnostic solution. The PITEX effort has considerable legacy in the NASA IVHM Technology Experiment for X-vehicles (NITEX) that was selected to fly on the X-34 subscale RLV that was being developed by Orbital Sciences Corporation. NITEX, funded through the Future-X Program Office, was to advance the technology-readiness level of selected IVHM technologies within a flight environment and to begin the transition of these technologies from experimental status into RLV baseline designs. The experiment was to perform realtime fault detection and isolation and suggest potential recovery actions for the X-34 main propulsion system (MPS) during all mission phases by using a combination of system-level analysis and detailed diagnostic algorithms.

Maul, William A.↗
NASA NTRS2006

Integrated System Health Management (ISHM) Technology Demonstration Project Final Report

Integrated System Health Management (ISHM) is an essential capability that will be required to enable upcoming explorations mission systems such as the Crew Exploration Vehicle (CEV) and Crew Launch Vehicle (CLV), as well as NASA aeronautics missions. However, the lack of flight experience and available test platforms have held back the infusion by NASA Ames Research Center (ARC) and the Jet Propulsion Laboratory (JPL) of ISHM technologies into future space and aeronautical missions. To address this problem, a pioneer project was conceived to use a high-performance aircraft as a low-cost proxy to develop, mature, and verify the effectiveness of candidate ISHM technologies. Given the similarities between spacecraft and aircraft, an F/A-18 currently stationed at Dryden Flight Research Center (DFRC) was chosen as a suitable host platform for the test bed. This report describes how the test bed was conceived, how the technologies were integrated on to the aircraft, and how these technologies were matured during the project. It also describes the lessons learned during the project and a forward path for continued work.

Mackey, Ryan↗
NASA NTRS2010

Monitoring System for Storm Readiness and Recovery of Test Facilities: Integrated System Health Management (ISHM) Approach

Severe weather events are likely occurrences on the Mississippi Gulf Coast. It is important to rapidly diagnose and mitigate the effects of storms on Stennis Space Center's rocket engine test complex to avoid delays to critical test article programs, reduce costs, and maintain safety. An Integrated Systems Health Management (ISHM) approach and technologies are employed to integrate environmental (weather) monitoring, structural modeling, and the suite of available facility instrumentation to provide information for readiness before storms, rapid initial damage assessment to guide mitigation planning, and then support on-going assurance as repairs are effected and finally support recertification. The system is denominated Katrina Storm Monitoring System (KStorMS). Integrated Systems Health Management (ISHM) describes a comprehensive set of capabilities that provide insight into the behavior the health of a system. Knowing the status of a system allows decision makers to effectively plan and execute their mission. For example, early insight into component degradation and impending failures provides more time to develop work around strategies and more effectively plan for maintenance. Failures of system elements generally occur over time. Information extracted from sensor data, combined with system-wide knowledge bases and methods for information extraction and fusion, inference, and decision making, can be used to detect incipient failures. If failures do occur, it is critical to detect and isolate them, and suggest an appropriate course of action. ISHM enables determining the condition (health) of every element in a complex system-of-systems or SoS (detect anomalies, diagnose causes, predict future anomalies), and provide data, information, and knowledge (DIaK) to control systems for safe and effective operation. ISHM capability is achieved by using a wide range of technologies that enable anomaly detection, diagnostics, prognostics, and advise for control: (1) anomaly detection algorithms and strategies, (2) fusion of DIaK for anomaly detection (model-based, numerical, statistical, empirical, expert-based, qualitative, etc.), (3) diagnostics/prognostics strategies and methods, (4) user interface, (5) advanced control strategies, (6) integration architectures/frameworks, (7) embedding of intelligence. Many of these technologies are mature, and they are being used in the KStorMS. The paper will describe the design, implementation, and operation of the KStorMS; and discuss further evolution to support other needs such as condition-based maintenance (CBM).

Figueroa, Fernando↗
NASA NTRS2011

Software Health Management with Bayesian Networks

Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults.

Mengshoel, Ole↗
NASA NTRS2003

Rocket Engine Health Management: Early Definition of Critical Flight Measurements

The NASA led Space Launch Initiative (SLI) program has established key requirements related to safety, reliability, launch availability and operations cost to be met by the next generation of reusable launch vehicles. Key to meeting these requirements will be an integrated vehicle health management ( M) system that includes sensors, harnesses, software, memory, and processors. Such a system must be integrated across all the vehicle subsystems and meet component, subsystem, and system requirements relative to fault detection, fault isolation, and false alarm rate. The purpose of this activity is to evolve techniques for defining critical flight engine system measurements-early within the definition of an engine health management system (EHMS). Two approaches, performance-based and failure mode-based, are integrated to provide a proposed set of measurements to be collected. This integrated approach is applied to MSFC s MC-1 engine. Early identification of measurements supports early identification of candidate sensor systems whose design and impacts to the engine components must be considered in engine design.

Christenson, Rick L.↗
NASA NTRS2002

The NASA Integrated Vehicle Health Management Technology Experiment for X-37

The NASA Integrated Vehicle Health Management (IVHM) Technology Experiment for X-37 was intended to run IVHM software on-board the X-37 spacecraft. The X-37 is intended to be an unpiloted vehicle that would orbit the Earth for up to 21 days before landing on a runway. The objectives of the experiment were to demonstrate the benefits of in-flight IVHM to the operation of a Reusable Launch Vehicle, to advance the Technology Readiness Level of this IVHM technology within a flight environment, and to demonstrate that the IVHM software could operate on the Vehicle Management Computer. The scope of the experiment was to perform real-time fault detection and isolation for X-37's electrical power system and electro-mechanical actuators. The experiment used Livingstone, a software system that performs diagnosis using a qualitative, model-based reasoning approach that searches system-wide interactions to detect and isolate failures. Two of the challenges we faced were to make this research software more efficient so that it would fit within the limited computational resources that were available to us on the X-37 spacecraft, and to modify it so that it satisfied the X-37's software safety requirements. Although the experiment is currently unfunded, the development effort had value in that it resulted in major improvements in Livingstone's efficiency and safety. This paper reviews some of the details of the modeling and integration efforts, and some of the lessons that were learned.

Schwabacher, Mark↗
NASA NTRS2018

Integrated System Health Management (ISHM) and Autonomy

Systems capabilities on ISHM (Integrated System Health Management) and autonomy have traditionally been addressed separately. This means that ISHM functions, such as anomaly detection, diagnostics, prognostics, and comprehensive system awareness have not been considered traditionally in the context of autonomy functions such as planning, scheduling, and mission execution. One key reason is that although they address systems capabilities, both ISHM and autonomy have traditionally individually been approached as independent strategies and models for analysis. Additionally, to some degree, a unified paradigm for ISHM and autonomy has been difficult to implement due to limitations of hardware and software. This paper explores a unified treatment of ISHM and autonomy in the context of distributed hierarchical autonomous operations.

Figueroa, Fernando↗
NASA NTRS2014

Ground Operations Autonomous Control and Integrated Health Management

An intelligent autonomous control capability has been developed and is currently being validated in ground cryogenic fluid management operations. The capability embodies a physical architecture consistent with typical launch infrastructure and control systems, augmented by a higher level autonomous control (AC) system enabled to make knowledge-based decisions. The AC system is supported by an integrated system health management (ISHM) capability that detects anomalies, diagnoses causes, determines effects, and could predict future anomalies. AC is implemented using the concept of programmed sequences that could be considered to be building blocks of more generic mission plans. A sequence is a series of steps, and each executes actions once conditions for the step are met (e.g. desired temperatures or fluid state are achieved). For autonomous capability, conditions must consider also health management outcomes, as they will determine whether or not an action is executed, or how an action may be executed, or if an alternative action is executed instead. Aside from health, higher level objectives can also drive how a mission is carried out. The capability was developed using the G2 software environment (www.gensym.com) augmented by a NASA Toolkit that significantly shortens time to deployment. G2 is a commercial product to develop intelligent applications. It is fully object oriented. The core of the capability is a Domain Model of the system where all elements of the system are represented as objects (sensors, instruments, components, pipes, etc.). Reasoning and decision making can be done with all elements in the domain model. The toolkit also enables implementation of failure modes and effects analysis (FMEA), which are represented as root cause trees. FMEA's are programmed graphically, they are reusable, as they address generic FMEA referring to classes of subsystems or objects and their functional relationships. User interfaces for integrated awareness by operators have been created.

Figueroa, Fernando↗
NASA NTRS2015

Intelligent Hardware-Enabled Sensor and Software Safety and Health Management for Autonomous UAS

Unmanned Aerial Systems (UAS) can only be deployed if they can effectively complete their mission and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. We propose to design a real-time, onboard system health management (SHM) capability to continuously monitor essential system components such as sensors, software, and hardware systems for detection and diagnosis of failures and violations of safety or performance rules during the ight 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- y temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power hardware realization using Field Programmable Gate Arrays (FPGAs) in order to avoid overburdening limited computing resources or costly re-certi cation of ight software due to instrumentation. No currently available SHM capabilities (or combinations of currently existing SHM capabilities) come anywhere close to satisfying these three criteria yet NASA will require such intelligent, hardwareenabled sensor and software safety and health management for introducing autonomous UAS into the National Airspace System (NAS). We propose a novel approach of creating modular building blocks for combining responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. Our proposed research program includes both developing this novel approach and demonstrating its capabilities using the NASA Swift UAS as a demonstration platform.

Robotics↗
NASA NTRS2007

A Support Database System for Integrated System Health Management (ISHM)

The development, deployment, operation and maintenance of Integrated Systems Health Management (ISHM) applications require the storage and processing of tremendous amounts of low-level data. This data must be shared in a secure and cost-effective manner between developers, and processed within several heterogeneous architectures. Modern database technology allows this data to be organized efficiently, while ensuring the integrity and security of the data. The extensibility and interoperability of the current database technologies also allows for the creation of an associated support database system. A support database system provides additional capabilities by building applications on top of the database structure. These applications can then be used to support the various technologies in an ISHM architecture. This presentation and paper propose a detailed structure and application description for a support database system, called the Health Assessment Database System (HADS). The HADS provides a shared context for organizing and distributing data as well as a definition of the applications that provide the required data-driven support to ISHM. This approach provides another powerful tool for ISHM developers, while also enabling novel functionality. This functionality includes: automated firmware updating and deployment, algorithm development assistance and electronic datasheet generation. The architecture for the HADS has been developed as part of the ISHM toolset at Stennis Space Center for rocket engine testing. A detailed implementation has begun for the Methane Thruster Testbed Project (MTTP) in order to assist in developing health assessment and anomaly detection algorithms for ISHM. The structure of this implementation is shown in Figure 1. The database structure consists of three primary components: the system hierarchy model, the historical data archive and the firmware codebase. The system hierarchy model replicates the physical relationships between system elements to provide the logical context for the database. The historical data archive provides a common repository for sensor data that can be shared between developers and applications. The firmware codebase is used by the developer to organize the intelligent element firmware into atomic units which can be assembled into complete firmware for specific elements.

FROM↗
NASA NTRS1993

Candidate Technologies for the Integrated Health Management Program

The purpose of this report is to assess Vehicle Health Management (VHM) technologies for implementation as a demonstration. Extensive studies have been performed to determine technologies which could be implemented on the Atlas and Centaur vehicles as part of a bridging program. This paper discusses areas today where VHM can be implemented for benefits in reliability, performance, and cost reduction. VHM Options are identified and one demonstration is recommended for execution.

Johnson, Neal F., Jr.↗
DOE OSTI2015

Progress Towards Prognostic Health Management of Passive Components in Advanced Reactors – Model Selection and Evaluation

This paper presents recent progress towards developing a prognostic health management framework for passive components of advanced reactors (AR). The focus of this paper is on lifecycle prognostics for passive components using a Bayesian prognostic algorithm that provides a natural framework for incorporating different sources of variability, and uncertainties inherent in the operations of AR. High-temperature creep damage, a prototypic failure mechanism in AR materials, is used as the context for this research. A Bayesian model selection approach is implemented to select the appropriate creep degradation model at any given time, using relevant sensor measurements reflecting the material degradation state. The model selection approach, based on reversible jump Markov chain Monte Carlo methods, is integrated with Bayesian particle filter-based prognostic framework. The proposed approach is evaluated using strain measurements obtained from accelerated creep testing of stainless steel specimens. Results indicate feasibility of the proposed approach in accurately identifying the creep degradation stage from the available measurements at a given time. Effect of uncertainties in material degradation model and measurement noise on the performance of the prognostic algorithm is also investigated.

advanced reactor prognostics↗
NASA NTRS2009

Software Health Management: A Short Review of Challenges and Existing Techniques

Modern spacecraft (as well as most other complex mechanisms like aircraft, automobiles, and chemical plants) rely more and more on software, to a point where software failures have caused severe accidents and loss of missions. Software failures during a manned mission can cause loss of life, so there are severe requirements to make the software as safe and reliable as possible. Typically, verification and validation (V&V) has the task of making sure that all software errors are found before the software is deployed and that it always conforms to the requirements. Experience, however, shows that this gold standard of error-free software cannot be reached in practice. Even if the software alone is free of glitches, its interoperation with the hardware (e.g., with sensors or actuators) can cause problems. Unexpected operational conditions or changes in the environment may ultimately cause a software system to fail. Is there a way to surmount this problem? In most modern aircraft and many automobiles, hardware such as central electrical, mechanical, and hydraulic components are monitored by IVHM (Integrated Vehicle Health Management) systems. These systems can recognize, isolate, and identify faults and failures, both those that already occurred as well as imminent ones. With the help of diagnostics and prognostics, appropriate mitigation strategies can be selected (replacement or repair, switch to redundant systems, etc.). In this short paper, we discuss some challenges and promising techniques for software health management (SWHM). In particular, we identify unique challenges for preventing software failure in systems which involve both software and hardware components. We then present our classifications of techniques related to SWHM. These classifications are performed based on dimensions of interest to both developers and users of the techniques, and hopefully provide a map for dealing with software faults and failures.

Pipatsrisawat, Knot↗