SEARCH · Search NASA

Results for “safety critical software”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14

All sources NASA NTRS DOE OSTI

NASA NTRS2007

A Software Safety Risk Taxonomy for Use in Retrospective Safety Cases

Safety standards contain technical and process-oriented safely requirements. The best time to include these requirements is early in the development lifecycle of the system. When software safety requirements are levied on a legacy system after the fact, a retrospective safety case will need to be constructed for the software in the system. This can be a difficult task because there may be few to no art facts available to show compliance to the software safely requirements. The risks associated with not meeting safely requirements in a legacy safely-critical computer system must be addressed to give confidence for reuse. This paper introduces a proposal for a software safely risk taxonomy for legacy safely-critical computer systems, by specializing the Software Engineering Institute's 'Software Development Risk Taxonomy' with safely elements and attributes.

Hill, Janice L.↗

NASA NTRS2007

Integrated Hardware and Software for No-Loss Computing

When an algorithm is distributed across multiple threads executing on many distinct processors, a loss of one of those threads or processors can potentially result in the total loss of all the incremental results up to that point. When implementation is massively hardware distributed, then the probability of a hardware failure during the course of a long execution is potentially high. Traditionally, this problem has been addressed by establishing checkpoints where the current state of some or part of the execution is saved. Then in the event of a failure, this state information can be used to recompute that point in the execution and resume the computation from that point. A serious problem arises when one distributes a problem across multiple threads and physical processors is that one increases the likelihood of the algorithm failing due to no fault of the scientist but as a result of hardware faults coupled with operating system problems. With good reason, scientists expect their computing tools to serve them and not the other way around. What is novel here is a unique combination of hardware and software that reformulates an application into monolithic structure that can be monitored in real-time and dynamically reconfigured in the event of a failure. This unique reformulation of hardware and software will provide advanced aeronautical technologies to meet the challenges of next-generation systems in aviation, for civilian and scientific purposes, in our atmosphere and in atmospheres of other worlds. In particular, with respect to NASA s manned flight to Mars, this technology addresses the critical requirements for improving safety and increasing reliability of manned spacecraft.

James, Mark↗

NASA NTRS2018

Verification and Validation Challenges for Adaptive Flight Control of Complex Autonomous Systems

Autonomy of aerospace systems requires the ability for flight control systems to be able to adapt to complex uncertain dynamic environment. In spite of the five decades of research in adaptive control, the fact still remains that currently no adaptive control system has ever been deployed on any safety-critical or human-rated production systems such as passenger transport aircraft. The problem lies in the difficulty with the certification of adaptive control systems since existing certification methods cannot readily be used for nonlinear adaptive control systems. Research to address the notion of metrics for adaptive control began to appear in the recent years. These metrics, if accepted, could pave a path towards certification that would potentially lead to the adoption of adaptive control as a future control technology for safety-critical and human-rated production systems. Development of certifiable adaptive control systems represents a major challenge to overcome. Adaptive control systems with learning algorithms will never become part of the future unless it can be proven that they are highly safe and reliable. Rigorous methods for adaptive control software verification and validation must therefore be developed to ensure that adaptive control system software failures will not occur, to verify that the adaptive control system functions as required, to eliminate unintended functionality, and to demonstrate that certification requirements imposed by regulatory bodies such as the Federal Aviation Administration (FAA) can be satisfied. This presentation will discuss some of the technical issues with adaptive flight control and related V&V challenges.

Nguyen, Nhan T.↗

NASA NTRS1990

Next manned transportation system

Avionics is the connecting link that integrates the hardware and software which satisfy systems requirements. Issues of the next manned transportation system are outlined: STS evolution; personnel launch system (PLS) sizing issues and constraints; PLS thermal protection system; PLS launch vehicle concepts; reliability and safety; advanced launch systems (ALS); near-term issues; avionics requirements; guideline to reduce operational costs; and critical human factors design considerations.

Erwin, Harry↗

NASA NTRS2019

Model Based Engineering for Software Assurance

NASA's successful development of next generation space vehicles, habitats, and robotic systems will require reliable hardware and software systems. The aim of this initiative is to develop modeling methodology and tools to support Model-Based Systems Engineering (MBSE) for software assurance and reliability analysis. This effort expands the Unified Modeling Language (UML) software design models to include fault data for the extraction of Failure Modes and Effects Criticality Analysis (FMECA) and Fault Tree Analysis (FTA) for software. We explored different modeling approaches to integrate the UML software design models with the Systems Modeling Language (SysML) system models to generate an integrated model and reliability tools that take into account software and hardware interfaces.The benefits of this concept directly affect the safety community with quick turnarounds to produce software assurance and reliability analysis artifacts and the ability to visualize failure effects, both hardware and software. The result is enhanced system design integrity and early identification of system risks. This initiative will enable software assurance activities early in the system design lifecycle, facilitating the discovery of design weaknesses and enhancing the capability to produce safe, hazard-free systems

Wang, Lui↗

NASA NTRSDate not supplied

Understanding and Verifying Neural Networks

Deep Neural Networks (DNNs) have gained immense popularity in recent times and have widespread use in applications such as image classification, sentiment analysis, speech recognition and also in safety-critical applications such as autonomous driving. However, they suffer limitations such as lack of explainability and robustness which raise safety and security concerns in their usage. Further, the complex structure and large input spaces of DNNs act as an impediment to thorough verification and testing. The SafeDNN project at the Robust Software Engineering (RSE) group at NASA aims at exploring techniques to ensure that systems that use deep neural networks are safe, robust and interpretable. In this talk, I will be presenting our technique Prophecy that automatically infers formal properties of deep neural network models. The tool extracts patterns based on neuron activations as preconditions that imply certain desirable output properties of the model. I would be highlighting case studies that use Prophecy in obtaining explanations for network decisions, understanding correct and incorrect behavior, providing formal guarantees wrt safety and robustness, and debugging neural network models. We have applied the tool on image classification networks, neural network controllers providing turn advisories in unmanned aircrafts, regression models used for autonomous center-line tracking in aircrafts and neural network object detectors

Deep Neural Networks↗

NASA NTRS1981

Reliability analysis of the F-8 digital fly-by-wire system

The F-8 Digital Fly-by-Wire (DFBW) flight test program intended to provide the technology for advanced control systems, giving aircraft enhanced performance and operational capability is addressed. A detailed analysis of the experimental system was performed to estimated the probabilities of two significant safety critical events: (1) loss of primary flight control function, causing reversion to the analog bypass system; and (2) loss of the aircraft due to failure of the electronic flight control system. The analysis covers appraisal of risks due to random equipment failure, generic faults in design of the system or its software, and induced failure due to external events. A unique diagrammatic technique was developed which details the combinatorial reliability equations for the entire system, promotes understanding of system failure characteristics, and identifies the most likely failure modes. The technique provides a systematic method of applying basic probability equations and is augmented by a computer program written in a modular fashion that duplicates the structure of these equations.

Brock, L. D.↗

NASA NTRS2009

Generating Code Review Documentation for Auto-Generated Mission-Critical Software

Model-based design and automated code generation are increasingly used at NASA to produce actual flight code, particularly in the Guidance, Navigation, and Control domain. However, since code generators are typically not qualified, there is no guarantee that their output is correct, and consequently auto-generated code still needs to be fully tested and certified. We have thus developed AUTOCERT, a generator-independent plug-in that supports the certification of auto-generated code. AUTOCERT takes a set of mission safety requirements, and formally verifies that the autogenerated code satisfies these requirements. It generates a natural language report that explains why and how the code complies with the specified requirements. The report is hyper-linked to both the program and the verification conditions and thus provides a high-level structured argument containing tracing information for use in code reviews.

Denney, Ewen↗

NASA NTRS2012

A Perspective on NASA Ames Air Traffic Management Research

This paper describes past and present air-traffic-management research at NASA Ames Research Center. The descriptions emerge from the perspective of a technical manager who supervised the majority of this research for the last four years. Past research contributions built a foundation for calculating accurate flight trajectories to enable efficient airspace management in time. That foundation led to two predominant research activities that continue to this day - one in automatically separating aircraft and the other in optimizing traffic flows. Today s national airspace uses many of the applications resulting from research at Ames. These applications include the nationwide deployment of the Traffic Management Advisor, new procedures enabling continuous descent arrivals, cooperation with industry to permit more direct flights to downstream way-points, a surface management system in use by two cargo carriers, and software to evaluate how well flights conform to national traffic management initiatives. The paper concludes with suggestions for prioritized research in the upcoming years. These priorities include: enabling more first-look operational evaluations, improving conflict detection and resolution for climbing or descending aircraft, and focusing additional attention on the underpinning safety critical items such as a reliable datalink.

Schroeder, Jeffery A.↗

NASA NTRS2015

NASA's Space Launch System Program Update

Hardware and software for the world's most powerful launch vehicle for exploration is being welded, assembled, and tested today in high bays, clean rooms and test stands across the United States. NASA's Space Launch System (SLS) continued to make significant progress in 2014 with more planned for 2015, including firing tests of both main propulsion elements and the program Critical Design Review (CDR). Developed with the goals of safety, affordability, and sustainability, SLS will still deliver unmatched capability for human and robotic exploration. The initial Block 1 configuration will deliver more than 70 metric tons of payload to low Earth orbit (LEO). The evolved Block 2 design will deliver some 130 metric tons to LEO. Both designs offer enormous opportunity and flexibility for larger payloads, simplifying payload design as well as ground and on-orbit operations, shortening interplanetary transit times, and decreasing overall mission risk. Over the past year, every vehicle element has manufactured or tested hardware. An RS-25 liquid propellant engine was hotfire-tested at NASA's Stennis Space Center, Miss. for the first time since 2009 exercising and validating the new engine controller, the renovated A-1 test stand, and the test teams. Four RS-25s will power the SLS core stage. A qualification five-segment solid rocket motor incorporating several design, material, and process changes was scheduled to be test-fired in March at the prime contractor's facility in Utah. The booster also successfully completed its Critical Design Review (CDR) validating the planned design. All six major manufacturing tools for the core stage are in place at the Michoud Assembly Facility in Louisiana, and have been used to build numerous pieces of confidence, qualification, and even flight hardware, including barrel sections, domes and rings used to assemble the world's largest rocket stage. SLS Systems Engineering accomplished several key tasks including vehicle avionics software and hardware build and testing, scale model acoustic and base heating tests. Construction of the Interim Cryogenic Propulsion Stage (ICPS) began. Advanced development provided a look into the future of SLS. Shell buckling knockdown factor testing refined decades-old design margins that added thousands of pounds to rocket payloads. Adaptive manufacturing and structured light scanning development promised to cut the cost and time associated with manufacturing and testing. This paper will provide an overview of the progress made over the past year and provide a glimpse of 2015 milestones and beyond on the way to the first launch in 2018.

May, Todd↗

NASA NTRS2015

NASA's Space Launch System Program Update

May, Todd↗

NASA NTRS2009

Deriving Safety Cases from Automatically Constructed Proofs

Formal proofs provide detailed justification for the validity of claims and are widely used in formal software development methods. However, they are often complex and difficult to understand, because the formalism in which they are constructed and encoded is usually machine-oriented, and they may also be based on assumptions that are not justified. This causes concerns about the trustworthiness of using formal proofs as arguments in safety-critical applications. Here, we present an approach to develop safety cases that correspond to formal proofs found by automated theorem provers and reveal the underlying argumentation structure and top-level assumptions. We concentrate on natural deduction style proofs, which are closer to human reasoning than resolution proofs, and show how to construct the safety cases by covering the natural deduction proof tree with corresponding safety case fragments. We also abstract away logical book-keeping steps, which reduces the size of the constructed safety cases. We show how the approach can be applied to the proofs found by the Muscadet prover.

Basir, Nurlida↗

NASA NTRS2020

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system and the aviation industry has experienced a steady decrease in fatalities over the years. This can be attributed to both improved flight critical systems with redundant hardware and software protections, as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main approach for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave within the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety, creating labels for the data requires huge amount of effort and is largely impractical. To address this challenge, we developed a Convolutional Variational Auto-Encoder (CVAE), which is an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach as well as unsupervised clustering-based approach using KMeans++ and kernel-based approach using One-Class Support Vector Machine (OC-SVM) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.

Memarzadeh, Milad↗

NASA NTRSDate not supplied

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system. The industry has experienced a steady decrease in fatalities over the years. This can be contributed to both improved flight critical systems with redundant hardware and software protections as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main practice for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave with the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety creating labels for the data requires huge amount of efforts and is largely expensive. As a result, in this article, we develop a Convolutional Variational Auto-Encoder (CVAE), an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach (as an upper bound) as well as an supervised clustering based on K-Means (as a lower bound) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.

Milad Memarzadeh↗

NASA NTRS1991

Portable Common Execution Environment (PCEE) project review: Peer review

The purpose of the review was to conduct an independent, in-depth analysis of the PCEE project and to provide the results of said review. The review team was tasked with evaluating the potential contribution of the PCEE project to the improvement of the life cycle support of mission and safety critical (MASC) computing components for large, complex, non-stop, distributed systems similar to those planned for such NASA programs as the space station, lunar outpost, and manned missions to Mars. Some conclusions of the review team are as follow: The PCEE project was given high marks for its breath of vision on the overall problem with MASC software; Correlated with the sweeping vision, the Review Team is very skeptical that any research project can successfully attack such a broad range of problems; and several recommendations are made such as to identify the components of the broad solution envisioned, prioritizing them with respect to their impact and the likely ability of the PCEE or others to attack them successfully, and to rewrite its Concept Document differentiating the problem description, objectives, approach, and results so that the project vision becomes assessible to others.

Locke, C. Douglass↗

NASA NTRS1981

Real-time computer simulation/emulation for verification of multi-fault-tolerant control of Centaur-in-Shuttle

NASA has contracted with General Dynamics to design and develop an advanced Centaur liquid upper stage for support of the Galileo and Solar Polar interplanetary missions in 1985-86. The control of the Centaur while it resides in the Shuttle cargo bay must meet the STS safety requirements to be dual failure tolerant in all mission critical functions. The demonstration of the integrity of this control system in the event of multiple component failures and worst-case time-phase asynchroniety among the system's computers is performed by a real-time computer simulation. The simulation emulates the control hardware, subsystem interfaces, and imbedded software processes, wire-by-wire, to provide accessibility for fault insertion. Observability is provided via graphics and diagnostic software. Verification is the product of Monte Carlo simulation analysis.

Szatkowski, G. P.↗

NASA NTRS2009

Ares I Avionics Introduction

The Ares I is the next generation human-rated launcher for the United States Constellation program. This system is required to provide single fault tolerance within defined crew safety and mission reliability limits. As part of the effort to achieve those safety goals, Ares I includes an avionics subsystem built as a multistring, voting architecture. The avionics design draws upon experience gained from building fly-by-wire systems for Shuttle, X- 38, and Seawolf. Architectural drivers for the avionics design include using proven technologies with existing suppliers of space rated parts for critical functions (to reduce overall development risk), easing the software development effort by using an off-theshelf, DO-178B certifiable, ARINC-653 operating system in the main flight computers, minimizing mutual data and power connections that might lead to a common-mode hardware failure of the redundant avionics strings, and centralizing overall Ares I command & control within the Upper Stage.

Marchant, Christopher C.↗

NASA NTRSDate not supplied

Cyber Resiliency and the Implementation of a Host-Based Intrusion Detection System in an Urban Air Mobility Environment

With the growth in Urban Air Mobility systems and the increasing reliance on interconnected technologies, ensuring the security of these complex components has become critical. As cities evolve into smart urban centers, the vulnerability to cyber threats escalates, possibly endangering citizens safety and the efficiency of transportation networks.In response to these challenges, this paper presents a study on the need for cyber resilient techniques within future air traffic environments. It will pay specific attention to the implementation of a Host-Based Intrusion Detection System (HIDS) utilizing Atomic OSSEC software, tailored specifically to a NASA simulation of an UrbanAirMobility environments’ unique demands. Further, this study seeks to outline the rational for NASA’s recommendation for a HIDS in such environments. It explores the design, development, and deployment of the proposed HIDS, focusing on its adaptability to monitor the hybrid nature of the Urban Air Mobility environment. Leveraging machine learning algorithms and anomaly detection techniques, the HIDS is equipped to continuously monitor and analyze the behavior of individual host systems, vehicles, and devices, thereby providing a proactive approach to threat detection. Implementing a HIDS is a pivotal strategy for enhancing cyber resiliency, as it gives an organization granular visibility into internal system activities, enables rapid detection and response to anomalous behavior and cyber threats, and fortifies the organizations overall cybersecurity posture. Finally, this study aims to provide recommendations and include learned takeaways that the Urban Air Mobility industry should consider. In brief, this paper highlights the significance of host-based intrusion detection in UrbanAirMobility environments and underscores the necessity of tailored security solutions to safeguard against emerging cyber threats.

UAM↗