SEARCH · Search NASA

Results for “Hazard Fault Tree”

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.

33 records · Page 2

All sources NASA NTRS DOE OSTI

NASA NTRS2011

Risk Acceptance Personality Paradigm: How We View What We Don't Know We Don't Know

The purpose of integrated hazard analyses, probabilistic risk assessments, failure modes and effects analyses, fault trees and many other similar tools is to give managers of a program some idea of the risks associated with their program. All risk tools establish a set of undesired events and then try to evaluate the risk to the program by assessing the severity of the undesired event and the likelihood of that event occurring. Some tools provide qualitative results, some provide quantitative results and some do both. However, in the end the program manager and his/her team must decide which risks are acceptable and which are not. Even with a wide array of analysis tools available, risk acceptance is often a controversial and difficult decision making process. And yet, today's space exploration programs are moving toward more risk based design approaches. Thus, risk identification and good risk assessment is becoming even more vital to the engineering development process. This paper explores how known and unknown information influences risk-based decisions by looking at how the various parts of our personalities are affected by what they know and what they don't know. This paper then offers some criteria for consideration when making risk-based decisions.

Massie, Michael J.↗

NASA NTRS1974

Operations analysis (study 2.1). Contingency analysis

Future operational concepts for the space transportation system were studied in terms of space shuttle upper stage failure contingencies possible during deployment, retrieval, or space servicing of automated satellite programs. Problems anticipated during mission planning were isolated using a modified 'fault tree' technique, normally used in safety analyses. A comprehensive space servicing hazard analysis is presented which classifies possible failure modes under the catagories of catastrophic collision, failure to rendezvous and dock, servicing failure, and failure to undock. The failure contingencies defined are to be taken into account during design of the upper stage.

Source record↗

NASA NTRS2019

Hazards Analysis and Failure Modes and Effects Criticality Analysis (FMECA) of Four Concept Vehicle Propulsion Systems

The primary objective of this research effort is to identify failure modes and hazards associated with the concept vehicles and to perform functional hazard analyses (FHA) and failure modes and effects criticality analyses (FMECA) for each. Boeing also created a Fault Tree Analysis (FTA) for each of the concept vehicles, as the FTA contains the connectivity between systems and is an accepted, top-down method to analyze the safety of an air-vehicle. Conceptual design of notional powertrain configuration for each of four (4) NASA RVLT (Revolutionary Vertical Lift Technology) Concept Vehicles were developed in as much detail as was necessary to support the reliability and safety analysis for this project. Functional block diagrams from each of the conceptual powertrain configurations were created and used to order the FHA, FMECA, and FTA. Hazards were identified and the severity of each were categorized in the FHA for use in a follow-up FMECA. The FTA took inputs from the FMECA and the functional block diagrams to develop the connectivity and develop a quantitative architecture that could be used to perform sensitivity studies, as related to vehicle safety.Guidelines for reliability targets for both the air vehicle and the operation in the UAM (Urban Air Mobility) mission are discussed. An industry literature search was performed in order to assess gaps in existing government regulations and industry specifications. The industry literature search led to air-vehicle and operational reliability discussions, as related to Distributed Electric/Hybrid-Electric Propulsion (DE/HEP) system operating in the UAM role. A discussion of results and recommendations for future work is also provided.

Hazards Analysis↗

NASA NTRS2006

Another Approach to Enhance Airline Safety: Using Management Safety Tools

The ultimate goal of conducting an accident investigation is to prevent similar accidents from happening again and to make operations safer system-wide. Based on the findings extracted from the investigation, the "lesson learned" becomes a genuine part of the safety database making risk management available to safety analysts. The airline industry is no exception. In the US, the FAA has advocated the usage of the System Safety concept in enhancing safety since 2000. Yet, in today s usage of System Safety, the airline industry mainly focuses on risk management, which is a reactive process of the System Safety discipline. In order to extend the merit of System Safety and to prevent accidents beforehand, a specific System Safety tool needs to be applied; so a model of hazard prediction can be formed. To do so, the authors initiated this study by reviewing 189 final accident reports from the National Transportation Safety Board (NTSB) covering FAR Part 121 scheduled operations. The discovered accident causes (direct hazards) were categorized into 10 groups Flight Operations, Ground Crew, Turbulence, Maintenance, Foreign Object Damage (FOD), Flight Attendant, Air Traffic Control, Manufacturer, Passenger, and Federal Aviation Administration. These direct hazards were associated with 36 root factors prepared for an error-elimination model using Fault Tree Analysis (FTA), a leading tool for System Safety experts. An FTA block-diagram model was created, followed by a probability simulation of accidents. Five case studies and reports were provided in order to fully demonstrate the usefulness of System Safety tools in promoting airline safety.

Lu, Chien-tsug↗

NASA NTRSDate not supplied

Global Landslide Hazard Assessment for Situational Awareness (LHASA) Version 2: New Activities and Future Plans

A remote sensing-based system has been developed to characterize the potential for rainfall-triggered landslides across the globe in near real-time. The Landslide Hazard Assessment for Situational Awareness (LHASA) model uses a decision tree framework to combine a static susceptibility map derived from information on slope, rock characteristics, forest loss, distance to fault zones and distance to road networks with satellite precipitation estimates from the Global Precipitation Measurement (GPM) mission. Since 2016, the LHASA model has been providing near real-time and retrospective estimates of potential landslide activity. Results of this work are available at https://landslides.nasa.gov. In order to advance LHASA’s capabilities to characterize landslide hazards and impacts dynamically, we have implemented a new approach that leverages machine learning, new parameters, and new inventories. LHASA 2.0 uses the XGBoost machine learning model to bring in dynamic variables as well as additional static variables to better represent landslide hazard globally. Global rainfall forecasts are also being evaluated to provide a 1-3 day forecast of potential landslide activity. Additional factors such as recent seismicity and burned areas are also being considered to represent the preconditioning or changing interactions with subsequent rainfall over affected areas. A series of parameters are being tested within this structure using NASA’s Global Landslide Catalog as well as many other event-based and multi-temporal inventories mapped by the project team or provided by project partners. In addition to estimates of landslide hazard, LHASA Version 2 will incorporate dynamic estimates of exposure including population, roads and infrastructure to highlight the potential impacts that rainfall-triggered landslides. The ultimate goal of LHASA Version 2.0 is to approximate the relative probabilities of landslide hazard and exposure across different space and time scales to inform hazard assessment retrospectively over the past 20 years, in near real-time, and in the future. In addition to the hazard. This presentation will outline the new activities for LHASA Version 2.0 and present some next steps for this system.

Dalia Kirschbaum↗

NASA NTRS2020

Enabling Assurance in the MBSE Environment

A number of specific benefits that fit within the hallmarks of effective development are realized with implementation of model-based approaches to systems and assurance. Model Based Systems Engineering (MBSE) enabled by standardized modeling languages (e.g., SysML®) is at the core. These benefits in the context of spaceflight system challenges can include [1]: • Improved management of complex development • Reduced risk in the development process • Improved cost management • Improved design decisions With appropriate modeling techniques the assurance community also can improve early oversight and insight into project development. NASA has shown the basic constructs of SysML in an MBSE environment offer several key advantages, within a Model Based Mission Assurance (MBMA) initiative [2, 3]. These include the following: • Model viewpoints that promote rapid and systematic assessment of requirements coverage, hazard tagging and risk management • Embedded safety assessments for launch vehicles • Deployment of model assisted development of reliability products - Failure Modes and Effects Analyses (FMEAs) and Fault Trees • Test Planning • Validation and Verification of complex functions • Support of Assurance Case development for complex systems In addition, while there are benefits to be harvested, there is a realization that these do not come without effort and cost. Enabling model-based approaches requires structure, not only in an organizational context, but in a modeling context as well. There can be a steep learning curve and costs associated to train skilled modelers. But, on the other hand, not all of the assurance community need to be modelers. Models themselves must conform to ontologies that enable assurance. This places constraints upon the models and modelers. Optimums have yet to be developed where resources and constraints on modeling must be traded off in the organization and modeling efforts for projects. A number of barriers need to be overcome, as well, which pose challenges to the developers of the software that supports MBSE/MBMA. Information and data must be made to flow seamlessly through the life cycle. Because there is a wide variety of tools used in the community, to avoid the problems of the past of silos, delays, and diverging interests, information should flow among these tools to support the “single source of truth” paradigm of MBSE. This will greatly facilitate MBMA and advancement of assurance functions.

Evans, John W.↗

NASA NTRS2006

Methodology for Designing Fault-Protection Software

A document describes a methodology for designing fault-protection (FP) software for autonomous spacecraft. The methodology embodies and extends established engineering practices in the technical discipline of Fault Detection, Diagnosis, Mitigation, and Recovery; and has been successfully implemented in the Deep Impact Spacecraft, a NASA Discovery mission. Based on established concepts of Fault Monitors and Responses, this FP methodology extends the notion of Opinion, Symptom, Alarm (aka Fault), and Response with numerous new notions, sub-notions, software constructs, and logic and timing gates. For example, Monitor generates a RawOpinion, which graduates into Opinion, categorized into no-opinion, acceptable, or unacceptable opinion. RaiseSymptom, ForceSymptom, and ClearSymptom govern the establishment and then mapping to an Alarm (aka Fault). Local Response is distinguished from FP System Response. A 1-to-n and n-to- 1 mapping is established among Monitors, Symptoms, and Responses. Responses are categorized by device versus by function. Responses operate in tiers, where the early tiers attempt to resolve the Fault in a localized step-by-step fashion, relegating more system-level response to later tier(s). Recovery actions are gated by epoch recovery timing, enabling strategy, urgency, MaxRetry gate, hardware availability, hazardous versus ordinary fault, and many other priority gates. This methodology is systematic, logical, and uses multiple linked tables, parameter files, and recovery command sequences. The credibility of the FP design is proven via a fault-tree analysis "top-down" approach, and a functional fault-mode-effects-and-analysis via "bottoms-up" approach. Via this process, the mitigation and recovery strategy(s) per Fault Containment Region scope (width versus depth) the FP architecture.

Barltrop, Kevin↗

NASA NTRS2022

A Dynamic Landslide Hazard Monitoring Framework for the Lower Mekong Region

The Lower Mekong region is one of the most landslide-prone areas of the world. Despite the need for dynamic characterization of landslide hazard zones within the region, it is largely understudied for several reasons. Dynamic and integrated understanding of landslide processes requires landslide inventories across the region, which have not been available previously. Computational limitations also hamper regional landslide hazard assessment, including accessing and processing remotely sensed information. Finally, open-source software and modelling packages are required to address regional landslide hazard analysis. Leveraging an open-source data-driven global Landslide Hazard Assessment for Situational Awareness model framework, this study develops a region-specific dynamic landslide hazard system leveraging satellite-based Earth observation data to assess landslide hazards across the lower Mekong region. A set of landslide inventories were prepared from high-resolution optical imagery using advanced image-processing techniques. Several static and dynamic explanatory variables (i.e., rainfall, soil moisture, slope, relief, distance to roads, distance to faults, distance to rivers) were considered during the model development phase. An extreme gradient boosting decision tree model was trained for the monsoon period of 2015–2019 and the model was evaluated with independent inventory information for the 2020 monsoon period. The model performance demonstrated considerable skill using receiver operating characteristic curve statistics, with Area Under the Curve values exceeding 0.95. The model architecture was designed to use near-real-time data, and it can be implemented in a cloud computing environment (i.e., Google Cloud Platform) for the routine assessment of landslide hazards in the Lower Mekong region. This work was developed in collaboration with scientists at the Asian Disaster Preparedness Center as part of the NASA SERVIR Program’s Mekong hub. The goal of this work is to develop a suite of tools and services on accessible open-source platforms that support and enable stakeholder communities to better assess landslide hazard and exposure at local to regional scales for decision making and planning.

Nishan Kumar Biswas↗

NASA NTRS2005

Knowledge Representation Standards and Interchange Formats for Causal Graphs

In many domains, automated reasoning tools must represent graphs of causally linked events. These include fault-tree analysis, probabilistic risk assessment (PRA), planning, procedures, medical reasoning about disease progression, and functional architectures. Each of these fields has its own requirements for the representation of causation, events, actors and conditions. The representations include ontologies of function and cause, data dictionaries for causal dependency, failure and hazard, and interchange formats between some existing tools. In none of the domains has a generally accepted interchange format emerged. The paper makes progress towards interoperability across the wide range of causal analysis methodologies. We survey existing practice and emerging interchange formats in each of these fields. Setting forth a set of terms and concepts that are broadly shared across the domains, we examine the several ways in which current practice represents them. Some phenomena are difficult to represent or to analyze in several domains. These include mode transitions, reachability analysis, positive and negative feedback loops, conditions correlated but not causally linked and bimodal probability distributions. We work through examples and contrast the differing methods for addressing them. We detail recent work in knowledge interchange formats for causal trees in aerospace analysis applications in early design, safety and reliability. Several examples are discussed, with a particular focus on reachability analysis and mode transitions. We generalize the aerospace analysis work across the several other domains. We also recommend features and capabilities for the next generation of causal knowledge representation standards.

Throop, David R.↗

NASA NTRS2013

Practical Application of PRA as an Integrated Design Tool for Space Systems

This paper presents the application of the first comprehensive Probabilistic Risk Assessment (PRA) during the design phase of a joint NASA/NOAA weather satellite program, Geostationary Operational Environmental Satellite Series R (GOES-R). GOES-R is the next generation weather satellite primarily to help understand the weather and help save human lives. PRA has been used at NASA for Human Space Flight for many years. PRA was initially adopted and implemented in the operational phase of manned space flight programs and more recently for the next generation human space systems. Since its first use at NASA, PRA has become recognized throughout the Agency as a method of assessing complex mission risks as part of an overall approach to assuring safety and mission success throughout project lifecycles. PRA is now included as a requirement during the design phase of both NASA next generation manned space vehicles as well as for high priority robotic missions. The influence of PRA on GOES-R design and operation concepts are discussed in detail. The GOES-R PRA is unique at NASA for its early implementation. It also represents a pioneering effort to integrate risks from both Spacecraft (SC) and Ground Segment (GS) to fully assess the probability of achieving mission objectives. PRA analysts were actively involved in system engineering and design engineering to ensure that a comprehensive set of technical risks were correctly identified and properly understood from a design and operations perspective. The analysis included an assessment of SC hardware and software, SC fault management system, GS hardware and software, common cause failures, human error, natural hazards, solar weather and infrastructure (such as network and telecommunications failures, fire). PRA findings directly resulted in design changes to reduce SC risk from micro-meteoroids. PRA results also led to design changes in several SC subsystems, e.g. propulsion, guidance, navigation and control (GNC), communications, mechanisms, and command and data handling (C&DH). The fault tree approach assisted in the development of the fault management system design. Human error analysis, which examined human response to failure, indicated areas where automation could reduce the overall probability of gaps in operation by half. In addition, the PRA brought to light many potential root causes of system disruptions, including earthquakes, inclement weather, solar storms, blackouts and other extreme conditions not considered in the typical reliability and availability analyses. Ultimately the PRA served to identify potential failures that, when mitigated, resulted in a more robust design, as well as to influence the program's concept of operations. The early and active integration of PRA with system and design engineering provided a well-managed approach for risk assessment that increased reliability and availability, optimized lifecyc1e costs, and unified the SC and GS developments.

Kalia, Prince↗

NASA NTRS2005

Failure Assessment

Three questions to which software developers want accurate, precise answers are "How can the software system fail?", "mat bad things will happen if the software fails?t', and "How many failures will the software experience?". Numerous techniques have been devised to answer these questions; three of the best known are: 1) Software Fault Tree Analysis (SFTA) 2) Software Failure Modes, Effects, and Criticality Analysis (SFMECA 3) Software Fault/Failure Modeling. SFTA and SFMECA have been successfully used to analyze the flight software for a number of robotic planetary exploration missions, including Galileo, Cassini, and Deep Space 1. Given the increasing interest in reusing software components from mission to mission, one of us has developed techniques for reusing the corresponding portions of the SFTA and SFMECA, reducing the effort required to conduct these analyses. SFTA has also been shown to be effective in analyzing the security aspects of software systems; intrusion mechanisms and effects can easily be modeled using these techniques. The Bi- Directional Safety Analysis (BDSA) method combines a forward search (similar to SFMECA) from potential failure modes to their effects, with a backward search (similar to SFTA) from feasible hazards to the contributing causes of each hazard. BDSA offers an efficient way to identify latent failures. Recent work has extended BDSA to product-line applications such as flight-instrumentation displays and developed tool support for the reuse of the failure-analysis artifacts within a product line. BDSA has also been streamlined to support those projects having tight cost and/or schedule constraints for their failure analysis efforts. We discuss lessons learned from practice, describe available tools, and identi@ some future directions for the topic. A substantial amount of research has been devoted to estimating the number of failures that a software system will experience during test and operations, as well as the number of faults that have been inserted into that system during its development. One of us has found that the amount of structural change to a system during its development is strongly related to the number of faults inserted into it. Using techniques requiring no additional effort on the part of the development organization, the required measurements of structural evolution can be easily obtained from a development effort's configuration management system and readily transformed into an estimate of fault content. So far, structure-fault relationships have been identified for source code; current work seeks to examine artifacts available earlier in the lifecycle to determine if similar relationships between structure and fault content can be found. In particular, relationships between requirements change requests and the number of faults inserted into the implemented system would provide a significant improvement in our ability to control software quality during the early development phases.

fault tree↗

NASA NTRS2011

Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners (Second Edition)

Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and logical analysis method aimed at identifying and assessing risks in complex technological systems for the purpose of cost-effectively improving their safety and performance. NASA's objective is to better understand and effectively manage risk, and thus more effectively ensure mission and programmatic success, and to achieve and maintain high safety standards at NASA. NASA intends to use risk assessment in its programs and projects to support optimal management decision making for the improvement of safety and program performance. In addition to using quantitative/probabilistic risk assessment to improve safety and enhance the safety decision process, NASA has incorporated quantitative risk assessment into its system safety assessment process, which until now has relied primarily on a qualitative representation of risk. Also, NASA has recently adopted the Risk-Informed Decision Making (RIDM) process [1-1] as a valuable addition to supplement existing deterministic and experience-based engineering methods and tools. Over the years, NASA has been a leader in most of the technologies it has employed in its programs. One would think that PRA should be no exception. In fact, it would be natural for NASA to be a leader in PRA because, as a technology pioneer, NASA uses risk assessment and management implicitly or explicitly on a daily basis. NASA has probabilistic safety requirements (thresholds and goals) for crew transportation system missions to the International Space Station (ISS) [1-2]. NASA intends to have probabilistic requirements for any new human spaceflight transportation system acquisition. Methods to perform risk and reliability assessment in the early 1960s originated in U.S. aerospace and missile programs. Fault tree analysis (FTA) is an example. It would have been a reasonable extrapolation to expect that NASA would also become the world leader in the application of PRA. That was, however, not to happen. Early in the Apollo program, estimates of the probability for a successful roundtrip human mission to the moon yielded disappointingly low (and suspect) values and NASA became discouraged from further performing quantitative risk analyses until some two decades later when the methods were more refined, rigorous, and repeatable. Instead, NASA decided to rely primarily on the Hazard Analysis (HA) and Failure Modes and Effects Analysis (FMEA) methods for system safety assessment.

Stamatelatos,Michael↗

NASA NTRS1992

Fault detection and fault tolerance in robotics

Robots are used in inaccessible or hazardous environments in order to alleviate some of the time, cost and risk involved in preparing men to endure these conditions. In order to perform their expected tasks, the robots are often quite complex, thus increasing their potential for failures. If men must be sent into these environments to repair each component failure in the robot, the advantages of using the robot are quickly lost. Fault tolerant robots are needed which can effectively cope with failures and continue their tasks until repairs can be realistically scheduled. Before fault tolerant capabilities can be created, methods of detecting and pinpointing failures must be perfected. This paper develops a basic fault tree analysis of a robot in order to obtain a better understanding of where failures can occur and how they contribute to other failures in the robot. The resulting failure flow chart can also be used to analyze the resiliency of the robot in the presence of specific faults. By simulating robot failures and fault detection schemes, the problems involved in detecting failures for robots are explored in more depth.

Visinsky, Monica↗

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 NTRS2021

Data-driven landslide nowcasting at the global scale

Landslides affect nearly every country in the world each year. To better understand this global hazard, the Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed previously. LHASA version 1 combines satellite precipitation estimates with a global landslide susceptibility map to produce a gridded map of potentially hazardous areas from 60° North-South every 3 h. LHASA version 1 categorizes the world’s land surface into three ratings: high, moderate, and low hazard with a single decision tree that first determines if the last seven days of rainfall were intense, then evaluates landslide susceptibility. LHASA version 2 has been developed with a data-driven approach. The global susceptibility map was replaced with a collection of explanatory variables, and two new dynamically varying quantities were added: snow and soil moisture. Along with antecedent rainfall, these variables modulated the response to current daily rainfall. In addition, the Global Landslide Catalog (GLC) was supplemented with several inventories of rainfall-triggered landslide events. These factors were incorporated into the machine-learning framework XGBoost, which was trained to predict the presence or absence of landslides over the period 2015–2018, with the years 2019–2020 reserved for model evaluation. As a result of these improvements, the new global landslide nowcast was twice as likely to predict the occurrence of historical landslides as LHASA version 1, given the same global false positive rate. Furthermore, the shift to probabilistic outputs allows users to directly manage the trade-off between false negatives and false positives, which should make the nowcast useful for a greater variety of geographic settings and applications. In a retrospective analysis, the trained model ran over a global domain for 5 years, and results for LHASA version 1 and version 2 were compared. Due to the importance of rainfall and faults in LHASA version 2, nowcasts would be issued more frequently in some tropical countries, such as Colombia and Papua New Guinea; at the same time, the new version placed less emphasis on arid regions and areas far from the Pacific Rim. LHASA version 2 provides a nearly real-time view of global landslide hazard for a variety of stakeholders.

XGBoos↗