SEARCH · Search NASA
Results for “Hazard analysis”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Virtual Assistant for First Responders Using Natural Language Understanding and Optical Character Recognition
Commercial deep learning capabilities are available for many applications such as computer vision processing and intelligent chat bots. The Google Cloud Platform product Google Dialogflow provides lifelike conversational artificial intelligence (AI) using machine learning (ML) to generate natural conversations between computers and humans. This ML utilizes natural language understanding (NLU) to recognize a user’s intent and extracts key information into a form of entities. We have developed a user-friendly application through understanding the hazardous material database, first aid safety guidelines and observing the process of first responders who access this information in the field. We created the Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL) virtual assistant using Dialogflow1 and TensorFlow2 paired with EasyOCR.3 The chatbot supports first responders by providing voice interaction which helps limit additional steps such as browsing through multiple categories when searching for information. Using feedback from our field interviews, the voice interface has been developed to enable the first responder to focus on the immediate emergency. With less distractions, the first responder is able to engage the incident more effectively. The partial hands-free TruePAL chatbot assistant improves the accessibility to the correct guidance by an average of 1.9 seconds compared to the widely used application, NIH WISER, which requires full attention to operate. We combined this intelligent chatbot with a separate visual processing capability to produce hazardous signage analysis and generate the proper guidance for first responders. With the evolving functionality of AI tools, the use of virtual assistants in first responder technology will be an advancement, benefiting the safety of both first responders and civilians.
Identifying Human Errors and Error Mechanisms From Accident Reports Using Large Language Models
Emerging operational concepts for aviation hinge on novel paradigms for human machine interaction. Critical to their safe operation is early consideration of human error into the design process. Existing methods for consideration of human error require significant expert input, which is challenging both in early design and in novel systems for which there is little existing safety expertise. In this research, we propose a methodology for identifying human error, error producing factors, and mechanisms in early design from historical incident reports. Additionally, we hypothesize that cross-domain sharing of lessons learned can aid with early design human considerations in circumstances where data is not relevant or incomplete. This is addressed by identifying causes of human error in aviation and railway domains through applying state-of-the art natural language processing techniques to historical incident reports. Using this method, it is possible to extract extensive reports on human error from past incidents. Using the proposed approach, we identify nine human errors from railway reports and fourteen from aviation reports, with three errors common to both domains. There is at least one error producing conditions for each human error while a majority of the errors have more than one error mechanism. We also found that a majority of the human errors, error producing factors, and error mechanisms (even if they are not common between the domains) can be used to inform safe operations across domains as long as the errors are not domain specific and are interpreted and contextualized using engineering judgement.
Defining A Modelling Language to Support Functional Hazard Assessment
Functional Hazard Assessment (FHA) is a key early-stage engineering process that supports the incorporation of safety in design by identifying the high-level functional hazards the system may encounter. While many FHA-like methodologies have been proposed in the design engineering literature, many of these methodologies have had difficulty becoming accepted industry practice. Industry standards, on the other hand, either provide little recommendation on how to represent the function of the system to perform FHA, or rely on readily-available models with little justification in design theory. This paper presents some of the problems with current modelling languages used for FHA which limit the scope, expressiveness, flexibility, and precision of the analysis, as well as desirable principles an FHA-supporting analysis language should embody. It further introduces the Functional Reasoning Design Language (FRDL), a formal modelling language for describing the functional behaviors of a system and their interactions which satisfies these principles. To demonstrate the use of this language, the modelling and hazard analysis of a disaster response drone is presented.
NASA System Safety Handbook: System Safety Framework and Concepts for Implementation - Volume 1
System safety assessment is defined in NPR 8715.3C, NASA General Safety Program Requirements as a disciplined, systematic approach to the analysis of risks resulting from hazards that can affect humans, the environment, and mission assets. Achievement of the highest practicable degree of system safety is one of NASA's highest priorities. Traditionally, system safety assessment at NASA and elsewhere has focused on the application of a set of safety analysis tools to identify safety risks and formulate effective controls.1 Familiar tools used for this purpose include various forms of hazard analyses, failure modes and effects analyses, and probabilistic safety assessment (commonly also referred to as probabilistic risk assessment (PRA)). In the past, it has been assumed that to show that a system is safe, it is sufficient to provide assurance that the process for identifying the hazards has been as comprehensive as possible and that each identified hazard has one or more associated controls. The NASA Aerospace Safety Advisory Panel (ASAP) has made several statements in its annual reports supporting a more holistic approach. In 2006, it recommended that "... a comprehensive risk assessment, communication and acceptance process be implemented to ensure that overall launch risk is considered in an integrated and consistent manner." In 2009, it advocated for "... a process for using a risk-informed design approach to produce a design that is optimally and sufficiently safe." As a rationale for the latter advocacy, it stated that "... the ASAP applauds switching to a performance-based approach because it emphasizes early risk identification to guide designs, thus enabling creative design approaches that might be more efficient, safer, or both." For purposes of this preface, it is worth mentioning three areas where the handbook emphasizes a more holistic type of thinking. First, the handbook takes the position that it is important to not just focus on risk on an individual basis but to consider measures of aggregate safety risk and to ensure wherever possible that there be quantitative measures for evaluating how effective the controls are in reducing these aggregate risks. The term aggregate risk, when used in this handbook, refers to the accumulation of risks from individual scenarios that lead to a shortfall in safety performance at a high level: e.g., an excessively high probability of loss of crew, loss of mission, planetary contamination, etc. Without aggregated quantitative measures such as these, it is not reasonable to expect that safety has been optimized with respect to other technical and programmatic objectives. At the same time, it is fully recognized that not all sources of risk are amenable to precise quantitative analysis and that the use of qualitative approaches and bounding estimates may be appropriate for those risk sources. Second, the handbook stresses the necessity of developing confidence that the controls derived for the purpose of achieving system safety not only handle risks that have been identified and properly characterized but also provide a general, more holistic means for protecting against unidentified or uncharacterized risks. For example, while it is not possible to be assured that all credible causes of risk have been identified, there are defenses that can provide protection against broad categories of risks and thereby increase the chances that individual causes are contained. Third, the handbook strives at all times to treat uncertainties as an integral aspect of risk and as a part of making decisions. The term "uncertainty" here does not refer to an actuarial type of data analysis, but rather to a characterization of our state of knowledge regarding results from logical and physical models that approximate reality. Uncertainty analysis finds how the output parameters of the models are related to plausible variations in the input parameters and in the modeling assumptions. The evaluation of unrtainties represents a method of probabilistic thinking wherein the analyst and decision makers recognize possible outcomes other than the outcome perceived to be "most likely." Without this type of analysis, it is not possible to determine the worth of an analysis product as a basis for making decisions related to safety and mission success. In line with these considerations the handbook does not take a hazard-analysis-centric approach to system safety. Hazard analysis remains a useful tool to facilitate brainstorming but does not substitute for a more holistic approach geared to a comprehensive identification and understanding of individual risk issues and their contributions to aggregate safety risks. The handbook strives to emphasize the importance of identifying the most critical scenarios that contribute to the risk of not meeting the agreed-upon safety objectives and requirements using all appropriate tools (including but not limited to hazard analysis). Thereafter, emphasis shifts to identifying the risk drivers that cause these scenarios to be critical and ensuring that there are controls directed toward preventing or mitigating the risk drivers. To address these and other areas, the handbook advocates a proactive, analytic-deliberative, risk-informed approach to system safety, enabling the integration of system safety activities with systems engineering and risk management processes. It emphasizes how one can systematically provide the necessary evidence to substantiate the claim that a system is safe to within an acceptable risk tolerance, and that safety has been achieved in a cost-effective manner. The methodology discussed in this handbook is part of a systems engineering process and is intended to be integral to the system safety practices being conducted by the NASA safety and mission assurance and systems engineering organizations. The handbook posits that to conclude that a system is adequately safe, it is necessary to consider a set of safety claims that derive from the safety objectives of the organization. The safety claims are developed from a hierarchy of safety objectives and are therefore hierarchical themselves. Assurance that all the claims are true within acceptable risk tolerance limits implies that all of the safety objectives have been satisfied, and therefore that the system is safe. The acceptable risk tolerance limits are provided by the authority who must make the decision whether or not to proceed to the next step in the life cycle. These tolerances are therefore referred to as the decision maker's risk tolerances. In general, the safety claims address two fundamental facets of safety: 1) whether required safety thresholds or goals have been achieved, and 2) whether the safety risk is as low as possible within reasonable impacts on cost, schedule, and performance. The latter facet includes consideration of controls that are collective in nature (i.e., apply generically to broad categories of risks) and thereby provide protection against unidentified or uncharacterized risks.
[High Pressure Gas Tanks]
Four high-pressure gas tanks, the basis of this study, were especially made by a private contractor and tested before being delivered to NASA Kennedy Space Center. In order to insure 100% reliability of each individual tank the staff at KSC decided to again submit the four tanks under more rigorous tests. These tests were conducted during a period from April 10 through May 8 at KSC. This application further validates the predictive safety model for accident prevention and system failure in the testing of four high-pressure gas tanks at Kennedy Space Center, called Continuous Hazard Tracking and Failure Prediction Methodology (CHTFPM). It is apparent from the variety of barriers available for a hazard control that some barriers will be more successful than others in providing protection. In order to complete the Barrier Analysis of the system, a Task Analysis and a Biomechanical Study were performed to establish the relationship between the degree of biomechanical non-conformities and the anomalies found within the system on particular joints of the body. This relationship was possible to obtain by conducting a Regression Analysis to the previously generated data. From the information derived the body segment with the lowest percentage of non-conformities was the neck flexion with 46.7%. Intense analysis of the system was conducted including Preliminary Hazard Analysis (PHA), Failure Mode and Effect Analysis (FMEA), and Barrier Analysis. These analyses resulted in the identification of occurrences of conditions, which may be becoming hazardous in the given system. These conditions, known as dendritics, may become hazards and could result in an accident, system malfunction, or unacceptable risk conditions. A total of 56 possible dendritics were identified. Work sampling was performed to observe the occurrence each dendritic. The out of control points generated from a Weighted c control chart along with a Pareto analysis indicate that the dendritics "Personnel not Wearing Proper Protective and Hose/tubing located in high-traffic area" which account for 59.18% of total dendritic frequency need to be addressed to reduce the chance of a hazard from occurring. However, the occurrences of some dendritics are more important than others. As a result immediate, from a Weighted c perspective, corrective action should be taken to ameliorate the cause of the Class A dendritic "Personnel located under suspended or moving loads" rather than just the most commonly occurring dendritics. In any case the vast majority of data obtained indicates that testing operations possess a relatively high degree of safety.
Predicting System Accidents with Model Analysis During Hybrid Simulation
Standard discrete event simulation is commonly used to identify system bottlenecks and starving and blocking conditions in resources and services. The CONFIG hybrid discrete/continuous simulation tool can simulate such conditions in combination with inputs external to the simulation. This provides a means for evaluating the vulnerability to system accidents of a system's design, operating procedures, and control software. System accidents are brought about by complex unexpected interactions among multiple system failures , faulty or misleading sensor data, and inappropriate responses of human operators or software. The flows of resource and product materials play a central role in the hazardous situations that may arise in fluid transport and processing systems. We describe the capabilities of CONFIG for simulation-time linear circuit analysis of fluid flows in the context of model-based hazard analysis. We focus on how CONFIG simulates the static stresses in systems of flow. Unlike other flow-related properties, static stresses (or static potentials) cannot be represented by a set of state equations. The distribution of static stresses is dependent on the specific history of operations performed on a system. We discuss the use of this type of information in hazard analysis of system designs.
The application of Skylab imagery to analysis of fault tectonics and earthquake hazards in the Peninsular Ranges, southern California
The author has identified the following significant results. Frame 114 of the Salton Sea area was studied in all bands to analyze the appearance of important faults. These faults were also studied in the field as well as from aircraft and in aerial photography. The San Andreas/Banning and the Mission Creek faults can be traced across Coachella Valley even though they are buried by alluvium. The faults form ground water barriers and the near surface ground water on the northeast sides of the faults supports patches of vegetation (mesquite and palms) in an otherwise barren desert. These oases are best seen in band 3 (color IR). Otherwise, faults are best seen in band 4 (aerial color). Of the B and W bands, 5 (red) is best for delineating faults. Bands 1 and 2 are excessively grainy and the resolution is considerably inferior to the other bands.
Launch site processing of hazardous payloads. Volume 3: Normal processing analysis
Explore the source record for details and available documents.
Launch site processing of hazardous payloads. Volume 4: Contingency processing analysis
Explore the source record for details and available documents.
Meteoroid hazard evaluation for simple structures with various orientations
Statistical analysis of spacecraft meteoroid hazards based on earth orbit about sun
Analysis of Possible Explosions at Kennedy Space Center Due to Spontaneous Ignition of Hypergolic Propellants
NASA's Constellation Program plan currently calls for the replacement of the Space Shuttle with the ARES I & V spacecraft and booster vehicles to send astronauts to the moon and beyond. Part of the ARES spacecraft is the Orion Crew Exploration Vehicle (CEV), which includes the Crew Module (CM) and Service Module (SM). The Orion CM's main propulsion system and supplies are provided by the SM. The SM is to be processed off line and moved to the Vehicle Assembly Building (V AB) for stacking to the first stage booster motors prior to ARES move to the launch pad. The new Constellation Program philosophy to process in this manner has created a major task for the KSC infrastructure in that conventional QD calculations are no longer viable because of the location of surrounding facilities near the VAB and the Multi Purpose Processing Facility (MPPF), where the SM will be serviced with nearly 18,000 pounds of hypergolic propellants. The Multi-Payload Processing Facility (MPPF) complex, constructed by NASA in 1994, is located just off E Avenue south of the Operations and Checkout (O&C) building in the Kennedy Space Center industrial area. The MPPF includes a high bay and a low bay. The MPPF high bay is 40.2 m (132 ft) long x 18.9 m (60 ft) wide with a ceiling height of 18.9 m (62 ft). The low bay is a 10.4 m (34 ft) long x 10.4 m (34 ft) wide processing area and has a ceiling height of6.1 m (20 ft). The MPPF is currently used to process non-hazardous payloads. Engineering Analysis Inc. (EAI), under contract with ASRC Aerospace, Inc. in conjunction with the Explosive Safety Office, NASA, Kennedy Space Center (KSC), has carried out an analysis of the effects of explosions at KSC in or near various facilities produced by the spontaneous ignition ofhypergolic fuel stored in the CEV SM. The facilities considered included (1) Vehicle Assembly Building (VAB) (2) Multi-Payload Processing Facility (MPPF) (3) Canister Rotation Facility (CRF) Subsequent discussion deals with the MPPF analysis. Figure 1 provides a view of the MPPF from the northwest. An interior view ofthe facility is shown in Figure 2. The study was concerned with both blast hazards and hazardous fragments which exceed existing safety standards, as described in Section 2.0. The analysis included both blast and fragmentation effects and was divided into three parts as follows: (1) blast (2) primary fragmentation (3) secondary fragmentation Blast effects are summarized in Section 3.0, primary fragmentation in Section 4.0, and secondary fragmentation (internal and external) in Section 5.0. Conclusions are provided in Section 6.0, while references cited are included in Section 7.0. A more detailed description of the entire study is available in a separate document.
A Model-Based Systems Engineering Evaluation of the Evolution to an In-Time Aviation Safety Management System
In 2018, as result of a recommendation from the National Academies, NASA began to prototype an In-Time Aviation Safety Management System (IASMS). The purpose of the IASMS is to enable innovative aviation operations and greater heterogeneity of the overall National Airspace (NAS) by automating much of the safety monitoring, assessment, and risk and hazard mitigation function present in today’s Safety Management Systems (SMS). NASA has worked together with early industry collaborators to understand how such a system might work and has published several early Concepts of Operation (ConOps) and other technical memoranda that illustrate the primary considerations for selected aviation domains. The shift from an SMS to an IASMS is predicated on several assumptions, including: 1.) automating safety functions will decrease the amount of time necessary for risk and hazard identification and analysis, making it more likely that safety concerns are understood ‘in-time’ to mitigate them, and 2.) an IASMS will allow easier tailoring of safety management processes to the particular risks and hazards inherent to that aviation operation. In this paper, we begin to validate these assumptions through the use of Model-Based Systems Engineering (MBSE).
A Model-Based Systems Engineering Evaluation of the Evolution to an In-Time Aviation Safety Management System
In 2018, as result of a recommendation from the National Academies, NASA began to prototype an In-Time Aviation Safety Management System(IASMS). The purpose of the IASMS is to enable innovative aviation operations and greater heterogeneity of the overall National Airspace (NAS) by automating much of the safety monitoring, assessment, and risk and hazard mitigation functionspresent in today’s Safety Management Systems (SMS). NASA has worked together with early industry collaborators to understand how such a system might work and has published several early Concepts of Operation (ConOps) and other technical memoranda that illustrate the primary considerations for selected aviation domains. The shift from an SMS to an IASMS is predicated on several assumptions, including: 1.) automating safety functions will decrease the amount of time necessary for risk and hazard identification and analysis, making it more likely that safety concerns are understood ‘in-time’ to mitigate them, and 2.) an IASMS will allow easier tailoring of safety management processes to the particular risks and hazards inherent to that aviation operation. In this paper, we begin to validate these assumptions through the use of Model-Based Systems Engineering (MBSE).
Computational Fluid Dynamics Simulation of A Hazardous Gas Purge System in an EUS Aft Adapter Umbilical Plate Concept
The Fluid Dynamics Branch at the NASA Marshall Space Flight Center actively supports flight programs through a variety of in-line analyses. One area of expertise is the mitigation of hazardous gas mixtures that may form during nominal vehicle fueling operation. After the successful Artemis I launch, design cycles for future iterations of the vehicle are in progress. The subject of this work concerns the analysis of a hazardous gas mitigation system concept for a future upper stage.
Computational Fluid Dynamics Simulation of A Hazardous Gas Purge System in an EUS Aft Adapter Umbilical Plate Concept
The Fluid Dynamics Branch at the NASA Marshall Space Flight Center actively supports flight programs through a variety of in-line analyses. One area of expertise is the mitigation of hazardous gas mixtures that may form during nominal vehicle fueling operation. After the successful Artemis I launch, design cycles for future iterations of the vehicle are in progress. The subject of this work concerns the analysis of a hazardous gas mitigation system concept for a future upper stage.
Statistical analysis of several terminal area traffic collision hazard factors.
An 11 hr sample of air traffic, comprising 584 tracks recorded at Atlanta during peak periods of August 1967, is analyzed to examine the statistical characteristics of range-guard intrusions and airspace conflicts in a terminal area. The number of intrusions (of an imaginary 3-naut mile, 500-ft range guard surrounding each aircraft) and number of conflicts (of the projected airspace for two aircraft) for a track exhibit Poisson variations with track duration. The hourly rate of intrusions follows the gas model square-law variation with traffic density, but the hourly conflict rate, contrary to the gas model, decreases with greater traffic density.
Application of system safety to rail transit systems
Management emphasis on system safety in the rapid transit industry includes the granting and use of funds by the Federal Government according to systematic analysis of safety hazards in advance. Likelihood predictions that those hazards will be activated by exposure of the system to a system failure, a human error, external conditions, or combinations of these aspects determine alternatives to the assumption of risk and recommend corrections before the system is operational. Rigorous safety analyses are projected to assure operational safety for prolonged periods under varied maintenance conditions; these analysis encompass station accident possibilities as well as train-person collisions, car equipment and design, traffic control systems, and tunnel design problems.