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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Development of a bayesian belief network model for the software reliability assessment of nuclear digital I and C safety systems - 206

Since the digital instrumentation and control systems are expected to play an important role for the safety systems in nuclear power plants (NPPs), the need has emerged to not only establish a basis for incorporating software behavior into digital I and C system reliability models, but also to quantify the failure probability of the software used in NPP digital protection systems. In this study, a Bayesian belief network (BBN) model is developed to quantitatively assess software reliability by estimating the number of faults in a software program considering its software development life cycle (SDLC). The model structure and parameters are established based on the information applicable to NPP safety-related systems and the evidence used to construct and quantify the BBN model was collected from three stages of expert elicitation. The software failure probability is estimated from the number of residual defects in a software program at the end of SDLC phase. As a case study, the BBN model was applied to quantify the software reliability of a typical digital protection software having the size of 50 function points and having the Medium development and validation and verification (V and V) qualities. The developed model can be applied to estimate the failure probability for both developing and deployed safety-related NPP software, and such results can be used to evaluate the quality of the digital I and C systems in addition to estimating potential reactor risk due to software failure. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Application of a bayesian belief network model to reliability assessment of nuclear safety-related software - 207

As the instrumentation and control (I and C) systems in nuclear power plants (NPPs) have been replaced with digital-based systems, the need to incorporate software failures into NPP probabilistic risk assessments has arisen. In order to assess the probability of software failure on demand, a Bayesian belief network (BBN) model was developed which estimates the number of defects and the resulting probability of software failure on demand in nuclear safety-related software. To assess the feasibility of the BBN framework, the BBN model was applied to the prototype Integrated Digital Protection System-Reactor Protection System (IDiPS-RPS) to estimate the number of remaining faults and the software failure probability of a target software. The developmental- and V and V-activities carried out during the IDiPS-RPS development process were evaluated based on the well-defined checklist derived by the V and V team and were estimated based on expert elicitation. In addition, the attribute evaluations and the number of FPs of the target software is provided as the inputs for the BBN model. The application results showed the feasibility of using BBNs for quantifying software failure probabilities and several insights were gained from the applications of the BBN model. The proposed BBN framework can be applied to estimate the software failure probability for other safety-related NPP software and provide an insight on modeling the software development process that involves iterations between different development phases. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2015

Development and Execution of the RUNSAFE Runway Safety Bayesian Belief Network Model

One focus area of the National Aeronautics and Space Administration (NASA) is to improve aviation safety. Runway safety is one such thrust of investigation and research. The two primary components of this runway safety research are in runway incursion (RI) and runway excursion (RE) events. These are adverse ground-based aviation incidents that endanger crew, passengers, aircraft and perhaps other nearby people or property. A runway incursion is the incorrect presence of an aircraft, vehicle or person on the protected area of a surface designated for the landing and take-off of aircraft; one class of RI events simultaneously involves two aircraft, such as one aircraft incorrectly landing on a runway while another aircraft is taking off from the same runway. A runway excursion is an incident involving only a single aircraft defined as a veer-off or overrun off the runway surface. Within the scope of this effort at NASA Langley Research Center (LaRC), generic RI, RE and combined (RI plus RE, or RUNSAFE) event models have each been developed and implemented as a Bayesian Belief Network (BBN). Descriptions of runway safety issues from the literature searches have been used to develop the BBN models. Numerous considerations surrounding the process of developing the event models have been documented in this report. The event models were then thoroughly reviewed by a Subject Matter Expert (SME) panel through multiple knowledge elicitation sessions. Numerous improvements to the model structure (definitions, node names, node states and the connecting link topology) were made by the SME panel. Sample executions of the final RUNSAFE model have been presented herein for baseline and worst-case scenarios. Finally, a parameter sensitivity analysis for a given scenario was performed to show the risk drivers. The NASA and LaRC research in runway safety event modeling through the use of BBN technology is important for several reasons. These include: 1) providing a means to clearly understand the cause and effect patterns leading to safety issues, incidents and accidents, 2) enabling the prioritization of specialty areas needing more attention to improve aviation safety, and 3) enabling the identification of gaps within NASA's Aviation Safety funding portfolio

Green, Lawrence L.↗
NASA NTRS2014

Development of a Bayesian Belief Network Runway Incursion Model

In a previous paper, a statistical analysis of runway incursion (RI) events was conducted to ascertain their relevance to the top ten Technical Challenges (TC) of the National Aeronautics and Space Administration (NASA) Aviation Safety Program (AvSP). The study revealed connections to perhaps several of the AvSP top ten TC. That data also identified several primary causes and contributing factors for RI events that served as the basis for developing a system-level Bayesian Belief Network (BBN) model for RI events. The system-level BBN model will allow NASA to generically model the causes of RI events and to assess the effectiveness of technology products being developed under NASA funding. These products are intended to reduce the frequency of RI events in particular, and to improve runway safety in general. The development, structure and assessment of that BBN for RI events by a Subject Matter Expert panel are documented in this paper.

Green, Lawrence L.↗
NASA NTRS2014

Modeling Increased Complexity and the Reliance on Automation: FLightdeck Automation Problems (FLAP) Model

This paper highlights the development of a model that is focused on the safety issue of increasing complexity and reliance on automation systems in transport category aircraft. Recent statistics show an increase in mishaps related to manual handling and automation errors due to pilot complacency and over-reliance on automation, loss of situational awareness, automation system failures and/or pilot deficiencies. Consequently, the aircraft can enter a state outside the flight envelope and/or air traffic safety margins which potentially can lead to loss-of-control (LOC), controlled-flight-into-terrain (CFIT), or runway excursion/confusion accidents, etc. The goal of this modeling effort is to provide NASA's Aviation Safety Program (AvSP) with a platform capable of assessing the impacts of AvSP technologies and products towards reducing the relative risk of automation related accidents and incidents. In order to do so, a generic framework, capable of mapping both latent and active causal factors leading to automation errors, is developed. Next, the framework is converted into a Bayesian Belief Network model and populated with data gathered from Subject Matter Experts (SMEs). With the insertion of technologies and products, the model provides individual and collective risk reduction acquired by technologies and methodologies developed within AvSP.

Ancel, Ersin↗
NASA NTRS2014

Development of a Bayesian Belief Network Runway Incursion and Excursion Model

In a previous work, a statistical analysis of runway incursion (RI) event data was conducted to ascertain the relevance of this data to the top ten Technical Challenges (TC) of the National Aeronautics and Space Administration (NASA) Aviation Safety Program (AvSP). The study revealed connections to several of the AvSP top ten TC and identified numerous primary causes and contributing factors of RI events. The statistical analysis served as the basis for developing a system-level Bayesian Belief Network (BBN) model for RI events, also previously reported. Through literature searches and data analysis, this RI event network has now been extended to also model runway excursion (RE) events. These RI and RE event networks have been further modified and vetted by a Subject Matter Expert (SME) panel. The combined system-level BBN model will allow NASA to generically model the causes of RI and RE events and to assess the effectiveness of technology products being developed under NASA funding. These products are intended to reduce the frequency of runway safety incidents/accidents, and to improve runway safety in general. The development and structure of the BBN for both RI and RE events are documented in this paper.

Green, Lawrence L.↗
NASA NTRS2005

Predicting Software Suitability Using a Bayesian Belief Network

The ability to reliably predict the end quality of software under development presents a significant advantage for a development team. It provides an opportunity to address high risk components earlier in the development life cycle, when their impact is minimized. This research proposes a model that captures the evolution of the quality of a software product, and provides reliable forecasts of the end quality of the software being developed in terms of product suitability. Development team skill, software process maturity, and software problem complexity are hypothesized as driving factors of software product quality. The cause-effect relationships between these factors and the elements of software suitability are modeled using Bayesian Belief Networks, a machine learning method. This research presents a Bayesian Network for software quality, and the techniques used to quantify the factors that influence and represent software quality. The developed model is found to be effective in predicting the end product quality of small-scale software development efforts.

Beaver, Justin M.↗
DOE OSTI2024

Application of a Geochemically Informed Leak Detection (GILD) Model to CO 2 Injection Sites on the United States Gulf Coast

The Gulf Coast region possesses great potential for CO 2 enhanced oil recovery (EOR) and CO 2 storage. A geochemically informed leak detection (GILD) model has been applied to CO 2 injection sites on the Gulf Coast with considerations of measurement variability. The Jasper aquifer in Montgomery County, Texas, was chosen to demonstrate the method. Based on background data from wells in the area, combinations of mineral and fluid compositions were used to create 23 scenarios for the geochemical model. The output from the geochemical model was used to identify sensitive monitoring species, and response functions were generated for these as a function of the CO 2 leakage concentration. The sources of measurement variability for background conditions were characterized from the Jasper aquifer background data, and then normalized using the coefficient of variation of each species across the monitoring wells. Bayesian belief network (BBN) models were constructed, and measurement variability of different levels were added to compare leak detection probabilities. Increasing measurement variability decreased the power to detect a leak of a given size. For a moderately high CO 2 concentration of 0.2 mol/kg, the probability of detecting this leakage effect using pH as the monitoring variable in an aquifer with calcite decreases from 98% (no measurement variability) to 61% (medium variability) to 33% (high variability). The loss in power of the sampling protocol with increasing measurement variability is similar in magnitude when Ca 2+ or HCO 3 - is used as the monitoring parameter, but only for aquifers with calcite.

58 GEOSCIENCES↗
DOE OSTI2016

Finite-Test-Case Development to Demonstrate Logical Integrity of Software in DRPS

Reactor protection system (RPS) in nuclear power plant (NPP) is in the process of digitalization in response to advanced features of digital systems. In this context, software integrity in the RPS becomes an important issue since it may cause common-cause failure. Therefore, to guarantee the safety of NPPs, the integrity of the software need to be properly demonstrated or assessed. There are three typical approaches for software reliability quantification: software reliability growth model (SRGM), Bayesian belief network (BBN), and test-based method. Basically, SRGM is difficult to apply to the safety-critical software because not only sufficient failure sets are unavailable but consequence of system failure is also unacceptable. Then the result obtained through the BBN model may have large uncertainty, so it produces difficulties in integrity demonstration of safety-critical software. Thus, the BBN result need to be complemented or verified by test-based method. Currently, for the integrity demonstration of safety-critical software, black-box test which feeds inputs and examines just outputs whether they succeeded or failed is considered based on trajectory-form-of-input (a series of successive values for the input variables that occur during the operation of the software over time). Usually these trajectory-inputs are randomly sampled from operational profile. In this approach, however, there are some practical difficulties. Therefore, we proposed a new test method for safety-critical software in digitalized reactor protection system (DRPS). The main contents described in this paper are about basic concepts under development of finite-test-cases by which the integrity of the software logic can be verified. It should be noted that the influence of the environments, such as operating system and other hardware, are not considered since the method focuses on software logic only. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2015

Risk-Based Causal Modeling of Airborne Loss of Separation

Maintaining safe separation between aircraft remains one of the key aviation challenges as the Next Generation Air Transportation System (NextGen) emerges. The goals of the NextGen are to increase capacity and reduce flight delays to meet the aviation demand growth through the 2025 time frame while maintaining safety and efficiency. The envisioned NextGen is expected to enable high air traffic density, diverse fleet operations in the airspace, and a decrease in separation distance. All of these factors contribute to the potential for Loss of Separation (LOS) between aircraft. LOS is a precursor to a potential mid-air collision (MAC). The NASA Airspace Operations and Safety Program (AOSP) is committed to developing aircraft separation assurance concepts and technologies to mitigate LOS instances, therefore, preventing MAC. This paper focuses on the analysis of causal and contributing factors of LOS accidents and incidents leading to MAC occurrences. Mid-air collisions among large commercial aircraft are rare in the past decade, therefore, the LOS instances in this study are for general aviation using visual flight rules in the years 2000-2010. The study includes the investigation of causal paths leading to LOS, and the development of the Airborne Loss of Separation Analysis Model (ALOSAM) using Bayesian Belief Networks (BBN) to capture the multi-dependent relations of causal factors. The ALOSAM is currently a qualitative model, although further development could lead to a quantitative model. ALOSAM could then be used to perform impact analysis of concepts and technologies in the AOSP portfolio on the reduction of LOS risk.

Geuther, Steven C.↗
DOE OSTI2017

Methodology on cyber security evaluation in nuclear facilities considering I and C architecture - 224

Cyber security has become a major issue in nuclear facilities recently due to obsolescence of existing analog system and increasing the use of digital system for instrumentation and control (I and C) system. There are several cases which are similar to cyber-attacks in nuclear facilities such as Davis-Besse NPP, Browns Ferry NPP, Hatch NPP, Natanz Nuclear Facility, and Monju NPP. Since the cyber-attack called 'Stuxnet' occurred to a Natanz Nuclear Facility at 2010, the regulatory agencies have developed regulatory guidance for cyber security of nuclear facilities. In this paper, we will propose a methodology on cyber security evaluation in nuclear facilities considering I and C architecture. We will introduce the cyber security evaluation model for I and C system with Bayesian Belief Network (BBN). The cyber security evaluation model for nuclear facilities consists of I and C architecture, cyber threat such as malicious activity of attacker, and mitigation measure against malicious activity. In order to make benchmark model with I and C architecture, cyber threat, and mitigation measure, the likelihood and consequence concepts are used as prior information to the model. Bayesian update can be used to evaluate the cyber security for nuclear facility by calculating posterior information and comparing between prior information and posterior information with the model. Moreover, we will propose the methodology on application of the cyber security to existing PSA by using the cyber security evaluation model with BBN. It presents quantitative information for cyber security against cyber-attack and it is helpful to communicate between licensee and regulator on nuclear facilities. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2014

Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions

Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper.

Luxhoj, J. T.↗
DOE OSTI2022

Dynamic Role-Based Access Control Policy for Smart Grid Applications: An Offline Deep Reinforcement Learning Approach

Role-based access control (RBAC) is adopted in the information and communication technology domain for authentication purposes. However, due to a very large number of entities within organizational access control (AC) systems, static RBAC management can be inefficient, costly, and can lead to cybersecurity threats. In this paper, a novel hybrid RBAC model is proposed, based on the principles of offline deep reinforcement learning (RL) and Bayesian belief networks. The considered framework utilizes a fully offline RL agent, which models the behavioral history of users as a Bayesian belief-based trust indicator. Thus, the initial static RBAC policy is improved in a dynamic manner through off-policy learning while guaranteeing compliance of the internal users with the security rules of the system. By deploying our implementation within the smart grid domain and specifically within a Distributed Energy Resources (DER) ecosystem, we provide an end-to-end proof of concept of our model. Finally, detailed analysis and evaluation regarding the offline training phase of the RL agent are provided, while the online deployment of the hybrid RL-based RBAC model into the DER ecosystem highlights its key operation features and salient benefits over traditional RBAC models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2005

Developing a Methodology for Eliciting Subjective Probability Estimates During Expert Evaluations of Safety Interventions: Application for Bayesian Belief Networks

The NASA Aviation Safety Program (AvSP) has defined several products that will potentially modify airline and/or ATC operations, enhance aircraft systems, and improve the identification of potential hazardous situations within the National Airspace System (NAS). Consequently, there is a need to develop methods for evaluating the potential safety benefit of each of these intervention products so that resources can be effectively invested to produce the judgments to develop Bayesian Belief Networks (BBN's) that model the potential impact that specific interventions may have. Specifically, the present report summarizes methodologies for improving the elicitation of probability estimates during expert evaluations of AvSP products for use in BBN's. The work involved joint efforts between Professor James Luxhoj from Rutgers University and researchers at the University of Illinois. The Rutgers' project to develop BBN's received funding by NASA entitled "Probabilistic Decision Support for Evaluating Technology Insertion and Assessing Aviation Safety System Risk." The proposed project was funded separately but supported the existing Rutgers' program.

Wiegmann, Douglas A.a↗
DOE OSTI2022

A Review of Bayesian Networks for Spatial Data

We report Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for integration of Bayesian networks with spatial processes.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

The Analysis of the Contribution of Human Factors to the In-Flight Loss of Control Accidents

In-flight loss of control (LOC) is currently the leading cause of fatal accidents based on various commercial aircraft accident statistics. As the Next Generation Air Transportation System (NextGen) emerges, new contributing factors leading to LOC are anticipated. The NASA Aviation Safety Program (AvSP), along with other aviation agencies and communities are actively developing safety products to mitigate the LOC risk. This paper discusses the approach used to construct a generic integrated LOC accident framework (LOCAF) model based on a detailed review of LOC accidents over the past two decades. The LOCAF model is comprised of causal factors from the domain of human factors, aircraft system component failures, and atmospheric environment. The multiple interdependent causal factors are expressed in an Object-Oriented Bayesian belief network. In addition to predicting the likelihood of LOC accident occurrence, the system-level integrated LOCAF model is able to evaluate the impact of new safety technology products developed in AvSP. This provides valuable information to decision makers in strategizing NASA's aviation safety technology portfolio. The focus of this paper is on the analysis of human causal factors in the model, including the contributions from flight crew and maintenance workers. The Human Factors Analysis and Classification System (HFACS) taxonomy was used to develop human related causal factors. The preliminary results from the baseline LOCAF model are also presented.

Ancel, Ersin↗
NASA NTRS2015

Bayesian Safety Risk Modeling of Human-Flightdeck Automation Interaction

Usage of automatic systems in airliners has increased fuel efficiency, added extra capabilities, enhanced safety and reliability, as well as provide improved passenger comfort since its introduction in the late 80's. However, original automation benefits, including reduced flight crew workload, human errors or training requirements, were not achieved as originally expected. Instead, automation introduced new failure modes, redistributed, and sometimes increased workload, brought in new cognitive and attention demands, and increased training requirements. Modern airliners have numerous flight modes, providing more flexibility (and inherently more complexity) to the flight crew. However, the price to pay for the increased flexibility is the need for increased mode awareness, as well as the need to supervise, understand, and predict automated system behavior. Also, over-reliance on automation is linked to manual flight skill degradation and complacency in commercial pilots. As a result, recent accidents involving human errors are often caused by the interactions between humans and the automated systems (e.g., the breakdown in man-machine coordination), deteriorated manual flying skills, and/or loss of situational awareness due to heavy dependence on automated systems. This paper describes the development of the increased complexity and reliance on automation baseline model, named FLAP for FLightdeck Automation Problems. The model development process starts with a comprehensive literature review followed by the construction of a framework comprised of high-level causal factors leading to an automation-related flight anomaly. The framework was then converted into a Bayesian Belief Network (BBN) using the Hugin Software v7.8. The effects of automation on flight crew are incorporated into the model, including flight skill degradation, increased cognitive demand and training requirements along with their interactions. Besides flight crew deficiencies, automation system failures and anomalies of avionic systems are also incorporated. The resultant model helps simulate the emergence of automation-related issues in today's modern airliners from a top-down, generalized approach, which serves as a platform to evaluate NASA developed technologies

Ancel, Ersin↗