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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
NASA NTRS2016

Certification Strategies using Run-Time Safety Assurance for Part 23 Autopilot Systems

Part 23 aircraft operation, and in particular general aviation, is relatively unsafe when compared to other common forms of vehicle travel. Currently, there exists technologies that could increase safety statistics for these aircraft; however, the high burden and cost of performing the requisite safety critical certification processes for these systems limits their proliferation. For this reason, many entities, including the Federal Aviation Administration, NASA, and the US Air Force, are considering new options for certification for technologies that will improve aircraft safety. Of particular interest, are low cost autopilot systems for general aviation aircraft, as these systems have the potential to positively and significantly affect safety statistics. This paper proposes new systems and techniques, leveraging run-time verification, for the assurance of general aviation autopilot systems, which would be used to supplement the current certification process and provide a viable path for near-term low-cost implementation. In addition, discussions on preliminary experimentation and building the assurance case for a system, based on these principles, is provided.

automatic collision avoidance↗
NASA NTRSDate not supplied

Effects of Communication Delay on Human Spaceflight Missions

Missions onboard the International Space Station rely on the real-time availability of a large ground team of system experts to command the vehicle, solve safety-critical problems, and guide the crew during complex operations. Also, in Low Earth Orbit (LEO), supplies can be sent and crews evacuated quite quickly if needed. Future missions Beyond Low Earth Orbit (BLEO) will not have this 24/7, real-time safety net as communication latency increases, resupply difficulty increases, and evacuation opportunities diminish. There are few, if any, terrestrial analogs for human spaceflight missions BLEO that reflect the conditions—including extreme environments, long mission durations, and small crew sizes – that make these missions so high risk. Studies on specific conditions, such as communication delays and asynchronous interactions, have been performed in NASA Earth-based analog missions and have found that communication delays can disrupt ground-crew interactions and adversely impact team performance. However, there are gaps and limitations in studies conducted to date, notably on human spacecraft system failure response and recovery, the impacts of shorter lunar-relevant communication delays on complex operations, and the effectiveness of countermeasures. The work presented here breaks down real anomalies that occurred on ISS and Apollo missions and creates example scenarios fort Lunar Surface and Mars missions to explore the impact of communication delays of varying length on onboard operations and mission outcomes. Our analyses indicate that short communication delays (e.g., seconds to a minute) adversely impact the ability for ground to provide real-time oversight and guidance and to catch quickly emerging problems in time. Longer communication delays (e.g., up to 40 minutes on Mars missions) call for a shift of responsibility for tactical operations from ground to crew; crew must make time-critical decisions independently and respond to time-critical vehicle anomalies to prevent consequences.

human-systems integration↗
NASA NTRSDate not supplied

Effects of Communication Delay on Human Spaceflight Missions

Missions onboard the International Space Station rely on the real-time availability of a large ground team of system experts to command the vehicle, solve safety-critical problems, and guide the crew during complex operations. Also, in Low Earth Orbit (LEO), supplies can be sent and crews evacuated quite quickly if needed. Future missions Beyond Low Earth Orbit (BLEO) will not have this 24/7, real-time safety net as communication latency increases, resupply difficulty increases, and evacuation opportunities diminish. There are few, if any, terrestrial analogs for human spaceflight missions BLEO that reflect the conditions—including extreme environments, long mission durations, and small crew sizes – that make these missions so high risk. Studies on specific conditions, such as communication delays and asynchronous interactions, have been performed in NASA Earth-based analog missions and have found that communication delays can disrupt ground-crew interactions and adversely impact team performance. However, there are gaps and limitations in studies conducted to date, notably on human spacecraft system failure response and recovery, the impacts of shorter lunar-relevant communication delays on complex operations, and the effectiveness of countermeasures. The work presented here breaks down real anomalies that occurred on ISS and Apollo missions and creates example scenarios fort Lunar Surface and Mars missions to explore the impact of communication delays of varying length on onboard operations and mission outcomes. Our analyses indicate that short communication delays (e.g., seconds to a minute) adversely impact the ability for ground to provide real-time oversight and guidance and to catch quickly emerging problems in time. Longer communication delays (e.g., up to 40 minutes on Mars missions) call for a shift of responsibility for tactical operations from ground to crew; crew must make time-critical decisions independently and respond to time-critical vehicle anomalies to prevent consequences.

human-systems integration↗
NASA NTRS1990

Payload deployment systems and advanced manipulators

The results of discussions on future development of avionics to support payload deployment systems and advanced manipulators are discussed. The discussions summarized here were held during the Space Transportation Avionics Technology Symposium in Williamsburg, Virginia on November 7 to 9, 1989. Symposium participants agreed that this subpanel would have benefitted from more participation by users. It was suggested that inputs from Shuttle payload users should be incorporated, either by direct discussions with users or by incorporating comments from users as kept by Payload Accommodations. The Jet Propulsion Laboratory (JPL), Goddard, and Langley, as builders of payloads, and the Space Station Utilization Office could also provide useful inputs. Other potential users for future systems should also be identified as early as possible to determine what they anticipate their needs to be. Symposium participants also recognized that payload deployment is normally not a safety critical area, and as such, is vulnerable to budget cuts that defer costs from development to operations. This does give opportunities for upgrades of operational systems, but these must be very cost effective to compete with vehicle requirements that enhance safety or increase lifetime.

Source record↗
NASA NTRS2019

Reliability Requirements and Research Strategies for Urban Air Mobility Propulsion

An emerging new mission for aeronautics is Urban Air Mobility (UAM), a concept for air transportation around metropolitan areas with passenger-carrying operations. UAM vehicles must be capable of vertical take-off and landing, and this requirement presents unique technical challenges for electric and hybrid-based vertical take-off and landing (eVTOL). A critical challenge for UAM market growth is to gain public acceptance for being as safe as - or safer than - commercial air travel and automotive transportation. There is a lack of data for propulsion systems, components, and the associated thermal management systems for UAM eVTOL propulsion systems. The new mission, new propulsion system concepts, safety criticality of propulsion component performance during vertical take-off and lift operations, and lack of data presents many research challenges and opportunities. NASA has developed and published UAM vehicle concept studies. For a subset of the said concept vehicles, NASA has contracted for a study to identify failure modes and hazards associated with the propulsion systems of the concept vehicles and to perform functional hazard analyses (FHA) and failure modes and effects criticality analyses (FMECA) for each. From the completed study results, it was recommended for NASA to support research toward developing electric/hybrid-electric propulsion components with improved reliability and to explore powertrain architectures that can take advantage of higher reliability components to achieve inherent air-vehicle safety. NASA has started a research effort for UAM propulsion with a focus toward improving safety and reliability. Recent results and research strategy will be discussed toward the goals by means of: 1) improving individual component reliability through advanced materials and design methods, 2) improving the thermal management system, and 3) designing propulsion system architectures to provide inherent UAM vehicle safety.

Krantz, Timothy↗
NASA NTRS1993

WTEC monograph on instrumentation, control and safety systems of Canadian nuclear facilities

This report updates a 1989-90 survey of advanced instrumentation and controls (I&C) technologies and associated human factors issues in the U.S. and Canadian nuclear industries carried out by a team from Oak Ridge National Laboratory (Carter and Uhrig 1990). The authors found that the most advanced I&C systems are in the Canadian CANDU plants, where the newest plant (Darlington) has digital systems in almost 100 percent of its control systems and in over 70 percent of its plant protection system. Increased emphasis on human factors and cognitive science in modern control rooms has resulted in a reduced workload for the operators and the elimination of many human errors. Automation implemented through digital instrumentation and control is effectively changing the role of the operator to that of a systems manager. The hypothesis that properly introducing digital systems increases safety is supported by the Canadian experience. The performance of these digital systems has been achieved using appropriate quality assurance programs for both hardware and software development. Recent regulatory authority review of the development of safety-critical software has resulted in the creation of isolated software modules with well defined interfaces and more formal structure in the software generation. The ability of digital systems to detect impending failures and initiate a fail-safe action is a significant safety issue that should be of special interest to nuclear utilities and regulatory authorities around the world.

Uhrig, Robert E.↗
NASA NTRS2019

Real-Time UAV Trajectory Prediction for Safety Monitoring in Low-Altitude Airspace

The rising number of small unmanned aerial vehicles (UAVs) expected in the next decade will enable a new series of commercial, service, and military operations in low altitude airspace as well as above densely populated areas. These operations may include on-demand delivery, medical transportation services, law enforcement operations, traffic surveillance and many more. Such unprecedented scenarios create the need for robust, efficient ways to monitor the UAV state in time to guarantee safety and mitigate contingencies throughout the operations. This work proposes a generalized monitoring and prediction methodology that utilizes realtime measurements of an autonomous UAV following a series of way-points. Two different methods, based on sinusoidal acceleration profiles and high-order splines, are utilized to generate the predicted path. The monitoring approach includes dynamic trajectory re-planning in the event of unexpected detour or hovering of the UAV during flight. It can be further extended to different vehicle types, to quantify uncertainty affecting the state variables, e.g., aerodynamic and other environmental effects, and can also be implemented to prognosticate safety-critical metrics which depend on the estimated flight path and required thrust. The proposed framework is implemented on a simplified, scalable UAV modeling and control system traversing 3D trajectories. Results presented include examples of real-time predictions of the UAV trajectories during flight and a critical analysis of the proposed scenarios under uncertainty constraints.

UAV trajectory prognosis↗
NASA NTRS2003

Associations between errors and contributing factors in aircraft maintenance

In recent years cognitive error models have provided insights into the unsafe acts that lead to many accidents in safety-critical environments. Most models of accident causation are based on the notion that human errors occur in the context of contributing factors. However, there is a lack of published information on possible links between specific errors and contributing factors. A total of 619 safety occurrences involving aircraft maintenance were reported using a self-completed questionnaire. Of these occurrences, 96% were related to the actions of maintenance personnel. The types of errors that were involved, and the contributing factors associated with those actions, were determined. Each type of error was associated with a particular set of contributing factors and with specific occurrence outcomes. Among the associations were links between memory lapses and fatigue and between rule violations and time pressure. Potential applications of this research include assisting with the design of accident prevention strategies, the estimation of human error probabilities, and the monitoring of organizational safety performance.

Professional Competence↗
NASA NTRS2010

Safety Arguments for Next Generation, Location Aware Computing

Concerns over accuracy, availability, integrity, and continuity have limited the integration of Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) for safety-critical applications. More recent augmentation systems, such as the European Geostationary Navigation Overlay Service (EGNOS) and the North American Wide Area Augmentation System (WAAS) have begun to address these concerns. Augmentation architectures build on the existing GPS/GLONASS infrastructures to support location based services in Safety of Life (SoL) applications. Much of the technical development has been directed by air traffic management requirements, in anticipation of the more extensive support to be offered by GPS III and Galileo. WAAS has already been approved to provide vertical guidance for aviation applications. During the next twelve months, the full certification of EGNOS for SoL applications is expected. This paper discusses similarities and differences between the safety assessment techniques used in Europe and North America.

Johnson, C. W.↗
NASA NTRS2004

Adding Assurance to Automatically Generated Code

Code to estimate position and attitude of a spacecraft or aircraft belongs to the most safety-critical parts of flight software. The complex underlying mathematics and abundance of design details make it error-prone and reliable implementations costly. AutoFilter is a program synthesis tool for the automatic generation of state estimation code from compact specifications. It can automatically produce additional safety certificates which formally guarantee that each generated program individually satisfies a set of important safety policies. These safety policies (e.g.. array-bounds, variable initialization) form a core of properties which are essential for high-assurance software. Here we describe the AutoFilter system and its certificate generator and compare our approach to the static analysis tool PolySpace.

Denney, Ewen↗
NASA NTRS2015

Application of SAE ARP4754A to Flight Critical Systems

This report documents applications of ARP4754A to the development of modern computer-based (i.e., digital electronics, software and network-based) aircraft systems. This study is to offer insight and provide educational value relative to the guidelines in ARP4754A and provide an assessment of the current state-of-the- practice within industry and regulatory bodies relative to development assurance for complex and safety-critical computer-based aircraft systems.

Peterson, Eric M.↗
NASA NTRSDate not supplied

Assurance Issues in Developing AI/ML Components (and their Standards) for Civil Aviation

Standards development activities require a keen and deep understanding of the problem being solved by the standard as well as the technologies being deployed in any reference implementation of the solution. It is important to understand the mechanisms and limits of the fundamental, underlying science of implementation and verification technologies used to realize and assure systems. We need to understand the limits of what current process and metrics can provide with respect to new technologies. US leadership is important in this endeavor, and it is vital that we have a measured approach that yields sound results. We wish to start with simple, well-defined, non-safety critical applications and then progress to functions which have (1) clearly defined requirements, (2) means of checking the answer/output, and (3) means of intervention and mitigation of incorrect answers/outputs.

Aviation Safety↗
NASA NTRS2013

Evidence Report: Risk of Inadequate Human-Computer Interaction

Human-computer interaction (HCI) encompasses all the methods by which humans and computer-based systems communicate, share information, and accomplish tasks. When HCI is poorly designed, crews have difficulty entering, navigating, accessing, and understanding information. HCI has rarely been studied in an operational spaceflight context, and detailed performance data that would support evaluation of HCI have not been collected; thus, we draw much of our evidence from post-spaceflight crew comments, and from other safety-critical domains like ground-based power plants, and aviation. Additionally, there is a concern that any potential or real issues to date may have been masked by the fact that crews have near constant access to ground controllers, who monitor for errors, correct mistakes, and provide additional information needed to complete tasks. We do not know what types of HCI issues might arise without this "safety net". Exploration missions will test this concern, as crews may be operating autonomously due to communication delays and blackouts. Crew survival will be heavily dependent on available electronic information for just-in-time training, procedure execution, and vehicle or system maintenance; hence, the criticality of the Risk of Inadequate HCI. Future work must focus on identifying the most important contributing risk factors, evaluating their contribution to the overall risk, and developing appropriate mitigations. The Risk of Inadequate HCI includes eight core contributing factors based on the Human Factors Analysis and Classification System (HFACS): (1) Requirements, policies, and design processes, (2) Information resources and support, (3) Allocation of attention, (4) Cognitive overload, (5) Environmentally induced perceptual changes, (6) Misperception and misinterpretation of displayed information, (7) Spatial disorientation, and (8) Displays and controls.

Kritina Holden↗
NASA NTRS2022

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs’ reliability. In response to these limitations, this paper proposes an application of the Bayesian deep learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation, and ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.

Kento Tomita↗
NASA NTRS2019

Human Performance Contributions to Safety in Commercial Aviation

Every day in aviation, pilots, air traffic controllers, and other front-line personnel perform countless correct judgments and actions in a variety of operational environments. These judgments and actions are often the difference between an accident and a non-event. Ironically, data on these behaviors are rarely collected or analyzed. Data-driven decisions about safety management and design of safety-critical systems are limited by the available data, which influence how decision makers characterize problems and identify solutions. Large volumes of data are collected on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that result in routine successful outcomes, safety management and system design decisions are based on a small sample of nonrepresentative safety data. This assessment aimed to find and document “safety successes” made possible by human operators. With many Aeronautics Research Mission Directorate (ARMD) Programs and Projects focusing on increased automation and autonomy and decreased human involvement, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant risk — a risk that has not been recognized to date. Without understanding how humans contribute to safety, any estimate of predicted safety of autonomous capabilities is incomplete and inherently suspect. Furthermore, understanding the ways in which humans contribute to safety can promote strategic interactions among safety technologies, functions, procedures and the people using them. Without this understanding, the full benefits of an integrated, optimized human/technology or autonomous system will not be realized. Historically, safety has been consistently defined in terms of the occurrence of accidents or recognized risks (i.e., in terms of things that go wrong). These adverse outcomes are explained by identifying their causes, and safety is restored by eliminating or mitigating these causes. An alternative to this approach is to focus on what goes right and identify how to replicate that process. Focusing on the rare cases of failures attributed to “human error” provides little information about why human performance routinely prevents adverse events. Hollnagel has proposed that things go right because people continuously adjust their work to match their operating conditions. These adjustments become increasingly important as systems continue to grow in complexity. Thus, the definition of safety should reflect not only “avoiding things that go wrong” but “ensuring that things go right.” The basis for safety management requires developing an understanding of everyday activities. However, few mechanisms to monitor everyday work exist in the aviation domain, which limits opportunities to learn how designs function in reality. This concept of safety thinking and safety management is reflected in the emerging field of resilience engineering. According to Hollnagel, a system is resilient if it can sustain required operations under expected and unexpected conditions by adjusting its functioning prior to, during, or following changes, disturbances, and opportunities. To explore “positive” behaviors that contribute to resilient performance in commercial aviation, the assessment team examined a range of existing sources of data about pilot and air traffic control (ATC) tower controller performance, including subjective interviews with domain experts and objective aircraft flight data records. These data were used to identify strategies that support resilient performance, methods for exploring and refining those strategies in existing data, and proposed methods for capturing and analyzing new data.

Null, Cynthia H.↗
NASA NTRS2019

In-Time Non-Participant Casualty Risk Assessment to Support Onboard Decision Making for Autonomous Unmanned Aircraft

Numerous operational paradigms, technologies, and missions are emerging as newcomers to the National Airspace System (NAS) develop small Unmanned Aircraft Systems (sUAS), personal air vehicles and other Urban Air Mobility (UAM) concepts. As the list of applications expands, maintaining the safety of the current airspace system remains one of the core concerns preventing widespread commercial implementation of these concepts. Further, the risks associated with unmanned aircraft operations themselves have to be recognized and mitigated in a timely manner. Safety-critical risks include, but are not limited to, flight outside of approved airspace, unsafe proximity to people or property, critical system failures, loss-of control, and cyber-security related risks. Instead of reacting to accidents, a set of predictive and data-driven risk monitoring, assessment, and mitigation capabilities are envisioned to help capture and eliminate hazards as these systems become operational. NASA’s System-wide Safety project is performing R&D on such a safety assurance concept. As part of this concept, this paper describes an architecture that continuously monitors a diverse set of onboard and ground-based sources to estimate and predict non-participant casualty risk during flight. Timely identification of the changing nature of this risk can inform decision making processes to mitigate current and impending situations.

Ancel, Ersin↗
NASA NTRS2011

Towards Measurement of Confidence in Safety Cases

Arguments in safety cases are predominantly qualitative. This is partly attributed to the lack of sufficient design and operational data necessary to measure the achievement of high-dependability targets, particularly for safety-critical functions implemented in software. The subjective nature of many forms of evidence, such as expert judgment and process maturity, also contributes to the overwhelming dependence on qualitative arguments. However, where data for quantitative measurements is systematically collected, quantitative arguments provide far more benefits over qualitative arguments, in assessing confidence in the safety case. In this paper, we propose a basis for developing and evaluating integrated qualitative and quantitative safety arguments based on the Goal Structuring Notation (GSN) and Bayesian Networks (BN). The approach we propose identifies structures within GSN-based arguments where uncertainties can be quantified. BN are then used to provide a means to reason about confidence in a probabilistic way. We illustrate our approach using a fragment of a safety case for an unmanned aerial system and conclude with some preliminary observations

Denney, Ewen↗