Integrated Systems Health Management includes as key elements fault detection, fault diagnostics, and failure prognostics. Whereas fault detection and diagnostics have been the subject of considerable emphasis in the Artificial Intelligence (AI) community in the past, prognostics has not enjoyed the same attention. The reason for this lack of attention is in part because prognostics as a discipline has only recently been recognized as a game-changing technology that can push the boundary of systems health management. This paper provides a survey of AI techniques applied to prognostics. The paper is an update to our previously published survey of data-driven prognostics.

A Survey of Artificial Intelligence for Prognostics.

Elevated temperatures can cause significant changes in guided-wave (GW) propagation and transduction for structural health monitoring (SHM). This work focuses on GW SHM using surface-bonded piezoelectric wafer transducers in metallic plates for the temperature range encountered in internal spacecraft structures (20—150°C). First, studies done to determine a suitable bonding agent are documented. This is then used in controlled experiments to examine changes in GW propagation and transduction using PZT-5A piezoelectric wafers under quasi-statically varying temperature (also from 20 to 150°C). Modeling efforts to explain the experimentally observed increase in time-of-flight and change in sensor response peak-to-peak magnitude with increasing temperature are detailed. Finally, these results are used in detection and location of mild and moderate damage using the pulse-echo GW testing approach within the temperature range.

Effects of Elevated Temperature on Guided-wave Structural Health Monitoring

In this paper, we introduce a new methodology for reasoning about the functional failures during early design of complex systems. The proposed approach is based on the notion that a failure happens when a functional element in the system does not perform its intended task. Accordingly, a functional criticality is defined depending on the role of functionality in accomplishing designed tasks. A simulation-based failure analysis tool is then used to analyze functional failures and reason about their impact on overall system functionality. The analysis results are then integrated into an early stage system architecture analysis framework that analyzes the impact of functional failures and their propagation to guide system-level architectural design decisions. With this method, a multitude of failure scenarios can be quickly analyzed to determine the effects of architectural design decisions on overall system functionality. Using this framework, design teams can systematically explore risks and vulnerabilities during the early (functional design) stage of system development prior to the selection of specific components. Application of the presented method to the design of a representative aerospace electrical power system (EPS) testbed demonstrates these capabilities.

A functional failure reasoning methodology for evaluation of conceptual system architectures

Digital twin for the structural health management of reusable spacecraft: A case study

The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities

System diagnosis is an integral part of any Integrated System Health Management application. Diagnostic applications make use of system information from the design phase, such as safety and mission assurance analysis, failure modes and effects analysis, hazards analysis, functional models, fault propagation models, and testability analysis. In modern process control and equipment monitoring systems, topological and analytic , models of the nominal system, derived from design documents, are also employed for fault isolation and identification. Depending on the complexity of the monitored signals from the physical system, diagnostic applications may involve straightforward trending and feature extraction techniques to retrieve the parameters of importance from the sensor streams. They also may involve very complex analysis routines, such as signal processing, learning or classification methods to derive the parameters of importance to diagnosis. The process that is used to diagnose anomalous conditions from monitored system signals varies widely across the different approaches to system diagnosis. Rule-based expert systems, case-based reasoning systems, model-based reasoning systems, learning systems, and probabilistic reasoning systems are examples of the many diverse approaches ta diagnostic reasoning. Many engineering disciplines have specific approaches to modeling, monitoring and diagnosing anomalous conditions. Therefore, there is no "one-size-fits-all" approach to building diagnostic and health monitoring capabilities for a system. For instance, the conventional approaches to diagnosing failures in rotorcraft applications are very different from those used in communications systems. Further, online and offline automated diagnostic applications are integrated into an operations framework with flight crews, flight controllers and maintenance teams. While the emphasis of this paper is automation of health management functions, striking the correct balance between automated and human-performed tasks is a vital concern.

https://ti.arc.nasa.gov/m/pub-archive/1094h/1094%20(APH).pdf

A Review of Diagnostic Techniques for ISHM Applications

The health state of a system is a critically important body of knowledge needed to safely operate space systems and effectively accomplish the mission. Vehicles and systems usually have a variety of methods for determining and annunciating the health state, from human observations to advanced processing of complex sensor systems. The goal of Integrated Vehicle Health Management systems is to provide a uniform body of knowledge of the health state from which all of the user needs can be met, including flight crew annunciation, flight controller information, maintenance data and procedure management. This paper presents a review of the issues and challenges, as well as solutions, to determining the total health state of a complex space vehicle. Both technological and procedural methods are discussed, to present a view of health management based on the needs of stakeholders, with the goal of health management systems providing the necessary information in the best way possible. Technology and architecture developments hold the promise of substantial reductions in operational costs and unprecedented levels of health state awareness to on-board crews, ground support teams and automated applications. System engineering of the vehicles and equipment needed for achieving the Space Exploration Vision must establish a balance of solutions taking advantage of available technology while keeping a focus on meeting the needs of the health state stakeholders.

A Review of System Health State Determination Methods

Current manned spaceflight programs, including the International Space Station (ISS) and the Space Shuttle, have a strong dependency on real-time support of ground personnel for the safe and successful execution of space missions. This system design and operations paradigm has proven to be largely successful over the last 40 years of manned spaceflight. However, NASA has set a new course towards exploring space beyond Low Earth Orbit (LEO) and short duration missions. The new Exploration Program vision includes extended surface operations on the Moon and Mars, which bring a new set of system design and operation challenges and requirements. The new challenges range from developing systems that enable a sustainable program over many years, to the technical challenges of long duration missions beyond LEO, like asynchronous communications with Earth-based support centers. Thus, the next generation spacecraft must have the capability to support critical, real-time decision-making on-board, enabling mission operations management that is not highly dependant on timely Earth-based support. Moreover, reliable and accurate system health state information is required for autonomous and/or interactive real-time decision-making and management of mission operations. The development of an optimal level of health management and automation capabilities on future spacecraft systems must start early in the design of such systems, and be directly influenced by system and mission requirements. Honeywell and other industry members have made significant technological advances in the area of Integrated Vehicle Health Management (IVHM). IVHM has provided significant life cycle savings in commercial and military aviation through improved reliability and lower operational costs. These benefits can also be applied to spacecraft, while building the foundation for automating other mission management functions. However, the use of an operations-relevant environment, such as the Mission Control Center environment, and the early involvement of the operations community in the development and evaluation of such technology is critical to the success of the development effort. It ensures that the developed capabilities have a direct benefit on crew safety, mission affordability, effectiveness, and are extensible to the needs of the next generation space systems. This can be accomplished by using current space flight programs, like ISS, as a platform to

Health Management and Automation for Future Space Systems

Methods and apparatus are provided for a model-based diagnostic interface. An apparatus is provided for a diagnostic interface for a system having system data, system information, and a system model having a model nomenclature, the diagnostic interface comprising at least one computational object producing an output responsive to said system data, wherein said at least one object includes a binding of said system data to said system information, wherein said system data is mapped to said model nomenclature before being bound. A method is provided for making a model-based diagnostic interface for a system having system information and system data representing the status of said system, the method comprising the steps of modeling said system to create a system model having a system model nomenclature, mapping said system data into said system model nomenclature, and binding said system data mapped to said system model nomenclature to said system information.

Model-based diagnostic interface for a vehicle health management system having a system model with a system nomeclature

*† ‡ Human-rated space flight systems are characterized by intense concerns for flight crew safety and for high costs of operations and development. Non-human-rated space flight systems associated with cargo or scientific/robotic payloads are only slightly different. They are characterized by concerns for reliability and performance as well as for high costs of operations and development. In the past, across the aerospace spectrum, significant efforts have been expended to try and influence the factors that contribute to level of safety, level of reliability, and cost of operations/development; but because these concerns often seem to be at odds, the manner in which the influencing efforts are applied is a tricky business. In the new NASA Exploration system-of-systems environment, where human and robotic elements must interact as part of a synergistic and interoperable whole, the necessity for these influencing efforts to move forward in as expedient, efficient, and integrated manner as possible, becomes an overall guiding principle. In this regard, Health Management (HM) technology has been touted as an approach that holds considerable promise as a method of simultaneously influencing these concerns in the proper directions. HM techniques have been around for a long time under various names and guises, and have broad applicability within the aerospace arena. However, in the area of human spaceflight, experience is limited to the early Mercury/Gemini/Apollo programs, the ongoing Space Shuttle/International Space Station (ISS) programs, and a handful of experimental/research & development programs, most notably the Orbital Space Plane (OSP) program. Nevertheless, a substantial amount of knowledge, experience, and lessons-learned from these programs, along with some information carefully extracted from the large repertoire of unmanned space and nonspace programs, can be applied to Project Constellation in the new NASA Exploration environment.

Applying Health Management Technology to the NASA Exploration System-of-Systems

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