The future of crewed deep space exploration requires advanced system awareness and monitoring techniques to ensure the reliability of critical subsystems such as the electric power system (EPS). Currently, expert systems are used to detect and isolate large abrupt faults such as short circuits are sufficient to safe the power system. More nuanced and subtle fault behavior is analyzed by ground control, where corrective and troubleshooting actions can be made remotely or by the onboard crew. To ensure the safety of the crew, onboard computers are tasked with the detection and diagnosis of faults and disturbances onboard the vehicle. Spacecraft experience additional failure modes on a regular basis after launch that were unplanned, unexpected, or ambiguous, and NASA has identified improved software as a means of lowering the number of failures onboard spacecraft. This paper proposes a novel combination of model-based and data-driven techniques to monitor and identify known normal, faulty, and anomalous behavior. The method is demonstrated against a high-fidelity simulation of the spacecraft EPS and validated against real hardware.

Prognostics of engineering systems or systems of systems is the prediction of future performance and/or the time at which one or more events of interest occur.Prognostics can be applied in a variety of applications, from spacecraft and aircraft to wind turbines, oil and gas infrastructure, and assembly lines.Prognostic results are used to inform action to extend life or prevent failures through changes in use or predictive maintenance.The NASA Prognostics Python Packages (ProgPy) (Teubert et al., 2022) are a set of opensourced Python packages supporting research and development of prognostics and health management for engineering systems, as described in (Goebel et al., 2017).ProgPy builds upon the architecture of the Matlab Prognostics Libraries (Daigle, 2016c(Daigle, , 2016a(Daigle, , 2016b)), Generic Software Architecture for Prognostics (Teubert et al., 2017), and Prognostics As-A-Service (Watkins et al., 2019).ProgPy implements architectures and common functionalities of prognostics, supporting both researchers and practitioners.

Maintaining safe operations in cyber physical systems is a complex task that must account for system degradation over time, since unexpected failures can result in the loss of life and property. Operational failures may be attributed to component degradation and disturbances in the environment that adversely impact system performance. Components in a CPS typically degrade at different rates, and, therefore, require continual monitoring to avoid unexpected failures. Moreover, the effects of multiple degrading components on system performance may be hard to predict. Developing and maintaining accurate physics-based system models can be expensive. Typically, it is infeasible to run a true system to failure, so researchers and practitioners have resorted to using data-driven techniques to better evaluate the effect of degrading components on overall system performance. However, sufficiently organized datasets of system operation are not readily available; the output of existing simulations is not organized to facilitate the use of data-driven machine learning techniques for prognostics. As a step toward addressing this problem, in this paper, we develop a data management framework and an end-to-end simulation testbed to generate such data. The framework facilitates the development and comparison of various system-level prognostics algorithms. We adopt a standard data-centered design methodology, combined with a model based engineering approach, to create a data management framework that address data integrity problems and facilitates the generation of reproducible results. We present an ontological design methodology centered around assets, processes, and data, and, as a proof of concept, develop an unmanned aerial vehicle (UAV) system operations database that captures operational data for UAVs with multiple degrading components operating in uncertain environments. Aim: The purpose of this work is to provide a systematic approach to data generation, curation, and storage that supports studies in fault management and system-level prognostics for real-world and simulated operations. We use a data-driven simulation-based approach to enable reliable and reproducible studies in system-level prognostics. This is accomplished with a data management methodology that enforces constraints on data types and interfaces, and decouples various parts of the simulation to enable proper links with related metadata. The goal is to provide a framework that facilitates data analysis and the development of data-driven models for prognostics using machine learning methods. We discuss the importance of systematic data management framework to support data generation with a simulation environment that generates operational data. We describe a standard framework for data management in the context of run-to-failure simulations, and develop a database schema and an API in MATLAB® and Python to support system-level prognostics analyses. Methods: A systematic approach to defining a data management framework for the study of prognostics applications is a central piece of this work. A second important contribution is the design of a Monte Carlo simulation environment to generate run to failure data for CPS with multiple degrading components. We adopt a bottom-up approach, starting with requirements and specifications, then move into functionality and constraints. With this framework, we use a Monte-Carlo simulation approach to generate data for developing and testing a variety of system-level prognostics algorithms. Results: We have developed a data management framework that can handle high dimensional and complex data generated from real or simulated systems for the study of prognostics. In this paper, we show the advantages of a well-organized data management framework for tracking high-fidelity data with high confidence for complex, dynamic CPS. Such frameworks impose data logging discipline and facilitate downstream uses for developing and comparing different data-driven monitoring, diagnostics, and system-level prognostics algorithms. Conclusions: In this paper, we demonstrate the design, development, and use of an asset, process, and data management framework for the research to develop prognostics & health management applications. This work helps fill a gap for system-level remaining useful life studies by providing a comprehensive simulation environment that can generate run to failure data, and a data management architecture that addresses the needs for system-level prognostics research. The framework is demonstrated with a Monte-Carlo simulation of a UAV system that operates multiple flights under different environmental conditions and degradation sources. This architecture for data management will enable researchers to conduct more complex experiments for a variety of cyber physical systems applications.

Computational models provide essential quantitative tools for assessing and predicting the health and performance of physical systems. However, high-fidelity models are rarely used in real-time operations or large optimization loops, due to their time-intensive nature. A common approach to improving computational efficiency of prognosis is to employ surrogate models. Such models can significantly decrease computation time for some accuracy loss. In this context, use of Dynamic Mode Decomposition (DMD) is proposed to generate surrogate models for lithium-ion (Li-ion) battery discharge. DMD has been suggested and used successfully in the area of fluid dynamics for over a decade, but it has not been applied to the Prognostics and Health Management domain, where farahead prediction of nonlinear behavior is crucial to propagate faults or predict Remaining Useful Life (RUL). For Li-ion battery health management, the standard application of DMD using only the observable quantities of interest was unable to capture the nonlinear discharge of batteries exhibited in lab testing. A potential solution was found by implementing Koopman theory, which considers the dynamics of nonlinear systems. Koopman theory provides a mechanism to trade-off low dimensional nonlinear models with high-dimensional linear ones in a DMD framework, by augmenting nonlinear state variables into the system representation. For battery health management, we augmented the observable variables with the hidden states of a higher-fidelity physics model to build the DMD surrogate. In comparison to a high-fidelity model, the surrogate improved computational efficiency with only a minimal loss of accuracy, and enabled long-term prognostics horizons. A generalized method for this was implemented in the ProgPy python packages.

The field of prognostics and health management provides quantitative methods for monitoring and predicting the health of physical systems. Prognostic algorithms are useful in that they can be employed to assess the current state of a system, propagate the system state throughout time, and pre-dict potential anomalies or failures that may occur. However, effective prognosis can be challenging to achieve in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for some prognostics applications. In this work, we implement three new algorithmic approaches to prediction (sampling meth-ods, variable prediction time step, variable prediction sample size) with the goal of improving computational efficiency while minimizing decrease in model accuracy. To quantitatively an-alyze our results, we examine a use-case of degradation of a Lithium-ion battery. Notably, through this work it was found that none of the sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. However, our results show that prediction accuracy is highly dependent on the time step used, and that an appropriate time step can optimize both model accuracy and simulation efficiency. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment in an application-specific manner may prove useful in some Applications. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency of prognosis.

An autonomous unmanned aerial system (UAS) needs, during the flight, accurate information about the current failure state of the aircraft and its capabilities in order to safely perform its mission and properly react to contingencies. The flight battery of an electric-propulsion aircraft is its most relevant resource. Model-based prognostics algorithms are used to obtain good estimates of its current state of charge and remaining capacity. However, these algorithms can have a large computational footprint. We present Prognostics-as-aService, a hybrid approach combining on-board computation with server-based prognostics on the ground. In this paper, we focus on the role, battery prognostics plays for the safe operation of a highly autonomous aircraft: prognostics for (1) continuous on-board safety monitoring, (2) for UAS operations, and (3) for contingency planning. We present the NASA Autonomous Operating System (AOS) and discuss how the autonomous components closely work together with on-board and server-based ground prognostics systems. We will illustrate the system with case studies on small NASA unmanned aircraft.

Notions of "teaming" between human and machine agents have emerged as a critical area of research, focused not on how machines can think like people, but on how machines can help people think better. This paper details the efforts of a NASA committee to develop a research plan to address challenges and technology gaps associated with future aviation market applications, focused specifically on identification and implementation of capabilities and principles that facilitate humans and machines working and thinking better together. The NASA committee reviewed existing human-autonomy teaming (HAT) research, identified stakeholder community objectives, reviewed relevant concepts of operation, and identified a framework for establishing a coordinated, comprehensive, and prioritized research plan. The committee's findings with regard to the importance of HAT and HAT challenges for enabling future aviation market applications are described. This effort is intended to provide policy makers, engineers, and researchers with useful guidance for directing and coordinating HAT research activities.

Comprehensive aircraft system health-state awareness is crit- ical for maintaining safe, efficient growth in global oper- ations, enabling higher levels of autonomy, and facilitat- ing new forms of aviation. Maintainers, vehicle operators, air traffic controllers, dispatchers, pilots, autonomous sys- tems, and other decision-makers must have reliable real-time knowledge of the vehicle health, the health of its critical com- posite systems, predictions of how health changes with time, and forecasts of how its capabilities change with health degra- dation to preserve safety and efficiency. Providing this infor- mation in a reliable manner in computationally constrained environments and across a wide range of vehicles and sys- tems continues to be a challenge. This challenge can be par- tially resolved through cloud computing, where the execu- tion of prognostic and diagnostic algorithms is performed on a network of remote servers hosted on the internet. NASA is developing a cloud computing service, Prognostics As-A- Service (PaaS), that explores the feasibility and challenges of cloud-enhanced prognostics. Though such a system has broad applicability, this research effort is focused on aviation applications.

Two methods were developed for online control design as part of a flight test effort to examine the feasibility of the NASA Learn-to-Fly concept. The methods use an aerodynamic model of the aircraft that is being identified in real-time onboard the aircraft to adjust the control parameters. One method employs adaptive nonlinear dynamic inversion, whereas the other consists of a classical autopilot structure. Effects from the interaction between the realtime modeling and the developed control laws are discussed. The Learn-to-Fly concept has been deemed feasible based on successful flights of both a stable and unstable aircraft.

Deep awareness of aircraft system health-state is critical for maintaining safe, efficient growth in global operations and enabling autonomy. Maintainers, operators, controllers, dispatchers, pilots, and autonomous systems must have reliable real-time predictions of vehicle health to preserve safety and efficiency. We will explore the feasibility and challenges of cloud enhanced prognostics. Aircraft request PaaS in flight to supplement onboard systems or provide complete health awareness. We will explore and demonstrate the ability to address six major challenges of PaaS: Generality, Environmental Complexity, Utility, Trust, Communications, and Security. We will also explore the factors in the decision to host prognostics onboard vs As-A-Service.

This paper investigates propsective architectures for decisionmaking in unmanned aerial systems. When these unmanned vehicles operate in urban environments, there are several sources of uncertainty that affect their behavior, and decisionmaking algorithms need to be robust to account for these different sources of uncertainty. It is important to account for several risk-factors that affect the flight of these unmanned systems, and facilitate decision-making by taking into consideration these various risk-factors. In addition, there are several technical challenges related to autonomous flight of unmanned aerial systems; these challenges include sensing, obstacle detection, path planning and navigation, trajectory generation and selection, etc. Many of these activities require significant computational power and in many situations, all of these activities need to be performed in real-time. In order to efficiently integrate these activities, it is important to develop a systematic architecture that can facilitate realtime decision-making. Four prospective architectures are discussed in this paper; on one end of the spectrum, the first architecture considers all activities/computations being performed onboard the vehicle whereas on the other end of the spectrum, the fourth and final architecture considers all activities/computations being performed in the cloud, using a new service known as “Prognostics as a Service” that is being developed at NASA Ames Research Center. The four different architectures are compared, their advantages and disadvantages are explained and conclusions are presented.

Prognostics is a systems engineering discipline focused on predicting end-of-life of components and systems. As a relatively new and emerging technology, there are few fielded implementations of prognostics, due in part to practitioners perceiving a large hurdle in developing the models, algorithms, architecture, and integration pieces. Similarly, no open software frameworks for applying prognostics currently exist. This paper introduces the Generic Software Architecture for Prognostics (GSAP), an open-source, cross-platform, object-oriented software framework and support library for creating prognostics applications. GSAP was designed to make prognostics more accessible and enable faster adoption and implementation by industry, by reducing the effort and investment required to develop, test, and deploy prognostics. This paper describes the requirements, design, and testing of GSAP. Additionally, a detailed case study involving battery prognostics demonstrates its use.

Readme for the Random Variable Toolbox usable manner. is a Web-based Git version control repository hosting service. It is mostly used for computer code. It offers all of the distributed version control and source code management (SCM) functionality of Git as well as adding its own features. It provides access control and several collaboration features such as bug tracking, feature requests, task management, and wikis for every project.[3] GitHub offers both plans for private and free repositories on the same account[4] which are commonly used to host open-source software projects.[5] As of April 2017, GitHub reports having almost 20 million users and 57 million repositories,[6] making it the largest host of source code in the world.[7] GitHub has a mascot called Octocat, a cat with five tentacles and a human-like face

Integration of Unmanned Aerial Vehicles (UAVs), autonomy, spacecraft, and other aviation technologies, in the airspace is becoming more and more complicated, and will continue to do so in the future. Inclusion of new technology and complexity into the airspace increases the importance and difficulty of safety assurance. Additionally, testing new technologies on complex aviation systems and systems of systems can be challenging, expensive, and at times unsafe when implementing real life scenarios. The application of prognostics to aviation and airspace management may produce new tools and insight into these problems. Prognostic methodology provides an estimate of the health and risks of a component, vehicle, or airspace and knowledge of how that will change over time. That measure is especially useful in safety determination, mission planning, and maintenance scheduling. In our research, we develop a live, distributed, hardware- in-the-loop Prognostics Virtual Laboratory testbed for aviation and airspace prognostics. The developed testbed will be used to validate prediction algorithms for the real-time safety monitoring of the National Airspace System (NAS) and the prediction of unsafe events. In our earlier work1 we discussed the initial Prognostics Virtual Laboratory testbed development work and related results for milestones 1 & 2. This paper describes the design, development, and testing of the integrated tested which are part of milestone 3, along with our next steps for validation of this work. Through a framework consisting of software/hardware modules and associated interface clients, the distributed testbed enables safe, accurate, and inexpensive experimentation and research into airspace and vehicle prognosis that would not have been possible otherwise. The testbed modules can be used cohesively to construct complex and relevant airspace scenarios for research. Four modules are key to this research: the virtual aircraft module which uses the X-Plane simulator and X-PlaneConnect toolbox, the live aircraft module which connects fielded aircraft using onboard cellular communications devices, the hardware in the loop (HITL) module which connects laboratory based bench-top hardware testbeds and the research module which contains diagnostics and prognostics tools for analysis of live air traffic situations and vehicle health conditions. The testbed also features other modules for data recording and playback, information visualization, and air traffic generation. Software reliability, safety, and latency are some of the critical design considerations in development of the testbed.

The Generic Software Architecture for Prognostics (GSAP) is a framework for applying prognostics. It makes applying prognostics easier by implementing many of the common elements across prognostic applications. The standard interface enables reuse of prognostic algorithms and models across systems using the GSAP framework.

We report on an optical sensor based on the physiological aspects of the eye and visionrelated neural layers of the common housefly (Musca domestica) that has been developed and built for aerospace applications. The intent of the research is to produce a position sensor with high functionality in low-light and low-contrast environments, high sensitivity to motion, and a compact size, which is lightweight, and has low power and computation requirements. The compound eye sensor uses a combination of overlapping photoreceptor responses that are well approximated by Gaussian distributions and neural superposition to detect image features, such as object motion, to a much higher degree than just the photoreceptor density would imply. The Gaussian overlap in the sensor comes from the front-end optical design, and the neural superposition is accomplished by subsequently combining the signals using analog electronics. The compound eye sensor is being developed to perform real-time tracking of a target on a flexible aircraft wing experiencing bending and torsion loads during flight. We report on results of laboratory experiments using the compound eye sensor to sense a target moving across its field of view.

The airspace is becoming more and more complicated, and will continue to do so in the future with the integration of Unmanned Aerial Vehicles (UAVs), autonomy, spacecraft, other forms of aviation technology into the airspace. The new technology and complexity increases the importance and difficulty of safety assurance. Additionally, testing new technologies on complex aviation systems & systems of systems can be very difficult, expensive, and sometimes unsafe in real life scenarios. Prognostic methodology provides an estimate of the health and risks of a component, vehicle, or airspace and knowledge of how that will change over time. That measure is especially useful in safety determination, mission planning, and maintenance scheduling. The developed testbed will be used to validate prediction algorithms for the real-time safety monitoring of the National Airspace System (NAS) and the prediction of unsafe events. The framework injects flight related anomalies related to ground systems, routing, airport congestion, etc. to test and verify algorithms for NAS safety. In our research work, we develop a live, distributed, hardware-in-the-loop testbed for aviation and airspace prognostics along with exploring further research possibilities to verify and validate future algorithms for NAS safety. The testbed integrates virtual aircraft using the X-Plane simulator and X-PlaneConnect toolbox, UAVs using onboard sensors and cellular communications, and hardware in the loop components. In addition, the testbed includes an additional research framework to support and simplify future research activities. It enables safe, accurate, and inexpensive experimentation and research into airspace and vehicle prognosis that would not have been possible otherwise. This paper describes the design, development, and testing of this system. Software reliability, safety and latency are some of the critical design considerations in development of the testbed. Integration of HITL elements in the development phases and veri cation/ validation are key elements to this report.

Current-Pressure (I/P) transducers are effective pressure regulators that can vary the output pressure depending on the supplied electrical current signal, and are commonly used in pneumatic actuators and valves. Faults in current-pressure transducers have a significant impact on the regulation mechanism; therefore, it is important to perform diagnosis to identify such faults and estimate their effect on the remaining useful life of the transducer. However, there are different sources of uncertainty that significantly affect the diagnostics procedure, and therefore, it may not be possible to perform fault diagnosis and prognosis accurately, with complete confidence. These sources of uncertainty include natural variability, sensor errors (gain, bias, noise), model uncertainty, etc. This paper presents a computational methodology to quantify the uncertainty and thereby estimate the confidence in the fault diagnosis of a current-pressure transducer. Further, the effect of diagnostic uncertainty on prognostics and remaining useful life prediction are also quantified. First, experiments are conducted to study the nominal and off-nominal behavior of the I/P transducer; however, sensor measurements are not fast enough to capture brief transient states that are indicative of wear, and hence, steady-state measurements are directly used for fault diagnosis. Second, the results of these experiments are used to train a Gaussian process model using machine learning principles. Finally, a Bayesian inference methodology is developed to quantify the uncertainty in fault diagnosis by systematically accounting for the aforementioned sources of uncertainty, and in turn, the uncertainty in prognostics is also estimated.