Results for “fault tree” — Page 6

DOE OSTI2015

TU-AB-BRD-01: Process Mapping

Current quality assurance and quality management guidelines provided by various professional organizations are prescriptive in nature, focusing principally on performance characteristics of planning and delivery devices. However, published analyses of events in radiation therapy show that most events are often caused by flaws in clinical processes rather than by device failures. This suggests the need for the development of a quality management program that is based on integrated approaches to process and equipment quality assurance. Industrial engineers have developed various risk assessment tools that are used to identify and eliminate potential failures from a system or a process before a failure impacts a customer. These tools include, but are not limited to, process mapping, failure modes and effects analysis, fault tree analysis. Task Group 100 of the American Association of Physicists in Medicine has developed these tools and used them to formulate an example risk-based quality management program for intensity-modulated radiotherapy. This is a prospective risk assessment approach that analyzes potential error pathways inherent in a clinical process and then ranks them according to relative risk, typically before implementation, followed by the design of a new process or modification of the existing process. Appropriate controls are then put in place to ensure that failures are less likely to occur and, if they do, they will more likely be detected before they propagate through the process, compromising treatment outcome and causing harm to the patient. Such a prospective approach forms the basis of the work of Task Group 100 that has recently been approved by the AAPM. This session will be devoted to a discussion of these tools and practical examples of how these tools can be used in a given radiotherapy clinic to develop a risk based quality management program. Learning Objectives: Learn how to design a process map for a radiotherapy process Learn how to perform failure modes and effects analysis analysis for a given process Learn what fault trees are all about Learn how to design a quality management program based upon the information obtained from process mapping, failure modes and effects analysis and fault tree analysis. Dunscombe: Director, TreatSafely, LLC and Center for the Assessment of Radiological Sciences; Consultant to IAEA and Varian Thomadsen: President, Center for the Assessment of Radiological Sciences Palta: Vice President of the Center for the Assessment of Radiological Sciences.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-AB-BRD-04: Development of Quality Management Program

Current quality assurance and quality management guidelines provided by various professional organizations are prescriptive in nature, focusing principally on performance characteristics of planning and delivery devices. However, published analyses of events in radiation therapy show that most events are often caused by flaws in clinical processes rather than by device failures. This suggests the need for the development of a quality management program that is based on integrated approaches to process and equipment quality assurance. Industrial engineers have developed various risk assessment tools that are used to identify and eliminate potential failures from a system or a process before a failure impacts a customer. These tools include, but are not limited to, process mapping, failure modes and effects analysis, fault tree analysis. Task Group 100 of the American Association of Physicists in Medicine has developed these tools and used them to formulate an example risk-based quality management program for intensity-modulated radiotherapy. This is a prospective risk assessment approach that analyzes potential error pathways inherent in a clinical process and then ranks them according to relative risk, typically before implementation, followed by the design of a new process or modification of the existing process. Appropriate controls are then put in place to ensure that failures are less likely to occur and, if they do, they will more likely be detected before they propagate through the process, compromising treatment outcome and causing harm to the patient. Such a prospective approach forms the basis of the work of Task Group 100 that has recently been approved by the AAPM. This session will be devoted to a discussion of these tools and practical examples of how these tools can be used in a given radiotherapy clinic to develop a risk based quality management program. Learning Objectives: Learn how to design a process map for a radiotherapy process Learn how to perform failure modes and effects analysis analysis for a given process Learn what fault trees are all about Learn how to design a quality management program based upon the information obtained from process mapping, failure modes and effects analysis and fault tree analysis. Dunscombe: Director, TreatSafely, LLC and Center for the Assessment of Radiological Sciences; Consultant to IAEA and Varian Thomadsen: President, Center for the Assessment of Radiological Sciences Palta: Vice President of the Center for the Assessment of Radiological Sciences.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-AB-BRD-02: Failure Modes and Effects Analysis

Current quality assurance and quality management guidelines provided by various professional organizations are prescriptive in nature, focusing principally on performance characteristics of planning and delivery devices. However, published analyses of events in radiation therapy show that most events are often caused by flaws in clinical processes rather than by device failures. This suggests the need for the development of a quality management program that is based on integrated approaches to process and equipment quality assurance. Industrial engineers have developed various risk assessment tools that are used to identify and eliminate potential failures from a system or a process before a failure impacts a customer. These tools include, but are not limited to, process mapping, failure modes and effects analysis, fault tree analysis. Task Group 100 of the American Association of Physicists in Medicine has developed these tools and used them to formulate an example risk-based quality management program for intensity-modulated radiotherapy. This is a prospective risk assessment approach that analyzes potential error pathways inherent in a clinical process and then ranks them according to relative risk, typically before implementation, followed by the design of a new process or modification of the existing process. Appropriate controls are then put in place to ensure that failures are less likely to occur and, if they do, they will more likely be detected before they propagate through the process, compromising treatment outcome and causing harm to the patient. Such a prospective approach forms the basis of the work of Task Group 100 that has recently been approved by the AAPM. This session will be devoted to a discussion of these tools and practical examples of how these tools can be used in a given radiotherapy clinic to develop a risk based quality management program. Learning Objectives: Learn how to design a process map for a radiotherapy process Learn how to perform failure modes and effects analysis analysis for a given process Learn what fault trees are all about Learn how to design a quality management program based upon the information obtained from process mapping, failure modes and effects analysis and fault tree analysis. Dunscombe: Director, TreatSafely, LLC and Center for the Assessment of Radiological Sciences; Consultant to IAEA and Varian Thomadsen: President, Center for the Assessment of Radiological Sciences Palta: Vice President of the Center for the Assessment of Radiological Sciences.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2011

Nuclear Material Control and Accountability System Effectiveness Tool (MSET)

A nuclear material control and accountability (MC&A) system effectiveness tool (MSET) has been developed in the United States for use in evaluating material protection, control, and accountability (MPC&A) systems in nuclear facilities. The project was commissioned by the National Nuclear Security Administration's Office of International Material Protection and Cooperation. MSET was developed by personnel with experience spanning more than six decades in both the U.S. and international nuclear programs and with experience in probabilistic risk assessment (PRA) in the nuclear power industry. MSET offers significant potential benefits for improving nuclear safeguards and security in any nation with a nuclear program. MSET provides a design basis for developing an MC&A system at a nuclear facility that functions to protect against insider theft or diversion of nuclear materials. MSET analyzes the system and identifies several risk importance factors that show where sustainability is essential for optimal performance and where performance degradation has the greatest impact on total system risk. MSET contains five major components: (1) A functional model that shows how to design, build, implement, and operate a robust nuclear MC&A system (2) A fault tree of the operating MC&A system that adapts PRA methodology to analyze system effectiveness and give a relative risk of failure assessment of the system (3) A questionnaire used to document the facility's current MPC&A system (provides data to evaluate the quality of the system and the level of performance of each basic task performed throughout the material balance area [MBA]) (4) A formal process of applying expert judgment to convert the facility questionnaire data into numeric values representing the performance level of each basic event for use in the fault tree risk assessment calculations (5) PRA software that performs the fault tree risk assessment calculations and produces risk importance factor reports on the facility's MC&A (software widely used in the aerospace, chemical, and nuclear power industries) MSET was peer reviewed in 2007 and validated in 2008 by benchmark testing at the Idaho National Laboratory in the United States. The MSET documents were translated into Russian and provided to Rosatom in July of 2008, and MSET is currently being evaluated for potential application in Russian Nuclear Facilities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Big Data For Operation and Maintenance Cost Reduction

The purpose of this research is to develop a first-of-a-kind framework for integrating Big Data capability into the daily activities of our current fleet of nuclear power plants. Big Data is traditionally defined as data sets with high volume, velocity, and heterogeneity, and the existing Big Data analytics capabilities are now widely popular in fields such as finance, weather, e-commerce, healthcare and sports. In the nuclear industry, while the volume and velocity of data may present computational challenges for existing analytics capabilities, data heterogeneity are seen to present the major challenge. This research project mainly focuses on incorporating the wide range of data heterogeneities in nuclear power plants into an integrated Big Data Analytics capability. The primary end-product of this project is a Big Data framework that is capable of dealing with the large volume and heterogeneity of the data found in nuclear power plants to extract timely and valuable information on equipment performance. The framework can generate system insights that are actionable relations between measurable impacts and the corresponding maintenance action plans and enable optimization of plant operation and maintenance based on the extracted information. The developed framework is capable of handling heterogeneous data including both image data and time-series sensor data. Specifically, this developed framework includes the following components. The first component is an overarching maintenance ontology which includes system insights required by maintenance optimization. The maintenance ontology interacts with other components in the developed framework. The second component handles Piping & Instrumentation Diagram (P&ID) data. It can be used to extract system components and their relations automatically from the P&IDs. This extracted information is stored in the first component, i.e., maintenance ontology, and is also used as input to the third component, i.e., a tool for generating the fault tree for the corresponding system. The generated fault tree in turn is stored in the ontology for assessing risk that is used as a criterion in maintenance policy optimization. The fourth component is a tool for inferring the parameters in the Markov degradation model for a nuclear system. It uses basic information from the ontology. The fifth component is a tool for assessing the degradation level using sensor measurement data, for example, pressure, flowrate. This tool can be used for determining corrective maintenance actions. The results obtained from components four and five are returned to the ontology. The sixth component of the framework is a tool for optimizing the maintenance policy for a nuclear system of interest. It takes certain basic information from the ontology, e.g., costs of maintenance actions and system failures, as input, and returns the optimal maintenance policy to the ontology. This tool can be used for determining predictive maintenance actions. A set of experiments have also been conducted to verify the algorithms developed in this project for nuclear system degradation monitoring. The experiments are based on four solenoid valves, similar to the ones used in nuclear power plants. The analyses based on the experimental data using two algorithms, i.e., the Randomized Window Decomposition (RWD) algorithm and the particle filtering algorithm, and the results are introduced in the report. The Big Data framework developed in this project can be used as a support tool in daily activities of plant operation and maintenance and will reduce current costs while maintaining or improving safety levels. Overall, the project will not only benefit existing reactors, however it will open new frontiers to realize the long overdue value of Big Data Analytics in the nuclear sphere.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2014

MO-E-9A-01: Risk Based Quality Management: TG100 In Action

One of the goals of quality management in radiation therapy is to gain high confidence that patients will receive the prescribed treatment correctly. To accomplish these goals professional societies such as the American Association of Physicists in Medicine (AAPM) has published many quality assurance (QA), quality control (QC), and quality management (QM) guidance documents. In general, the recommendations provided in these documents have emphasized on performing device-specific QA at the expense of process flow and protection of the patient against catastrophic errors. Analyses of radiation therapy incidents find that they are most often caused by flaws in the overall therapy process, from initial consult through final treatment, than by isolated hardware or computer failures detectable by traditional physics QA. This challenge is shared by many intrinsically hazardous industries. Risk assessment tools and analysis techniques have been developed to define, identify, and eliminate known and/or potential failures, problems, or errors, from a system, process and/or service before they reach the customer. These include, but are not limited to, process mapping, failure modes and effects analysis (FMEA), fault tree analysis (FTA), and establishment of a quality management program that best avoids the faults and risks that have been identified in the overall process. These tools can be easily adapted to radiation therapy practices because of their simplicity and effectiveness to provide efficient ways to enhance the safety and quality of treatment processes. Task group 100 (TG100) of AAPM has developed a risk-based quality management program that uses these tools. This session will be devoted to a discussion of these tools and how these tools can be used in a given radiotherapy clinic to develop a risk based QM program. Learning Objectives: Learn how to design a process map for a radiotherapy process. Learn how to perform a FMEA analysis for a given process. Learn what Fault tree analysis is all about. Learn how to design a quality management program based upon the information obtained from process mapping, FMEA and FTA.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2004

Derivation of Failure Rates and Probability of Failures for the International Space Station Probabilistic Risk Assessment Study

National Aeronautics and Space Administration s (NASA) International Space Station (ISS) Program uses Probabilistic Risk Assessment (PRA) as part of its Continuous Risk Management Process. It is used as a decision and management support tool to not only quantify risk for specific conditions, but more importantly comparing different operational and management options to determine the lowest risk option and provide rationale for management decisions. This paper presents the derivation of the probability distributions used to quantify the failure rates and the probability of failures of the basic events employed in the PRA model of the ISS. The paper will show how a Bayesian approach was used with different sources of data including the actual ISS on orbit failures to enhance the confidence in results of the PRA. As time progresses and more meaningful data is gathered from on orbit failures, an increasingly accurate failure rate probability distribution for the basic events of the ISS PRA model can be obtained. The ISS PRA has been developed by mapping the ISS critical systems such as propulsion, thermal control, or power generation into event sequences diagrams and fault trees. The lowest level of indenture of the fault trees was the orbital replacement units (ORU). The ORU level was chosen consistently with the level of statistically meaningful data that could be obtained from the aerospace industry and from the experts in the field. For example, data was gathered for the solenoid valves present in the propulsion system of the ISS. However valves themselves are composed of parts and the individual failure of these parts was not accounted for in the PRA model. In other words the failure of a spring within a valve was considered a failure of the valve itself.

Vitali, Roberto↗

DOE OSTI2022

A Causal Approach to Integrate Component Health Data into System Reliability Models

Two of the challenges of current plant reliability approaches are the ability to integrate plant health data, and to support decision making. Condition based data and diagnostic/prognostic information are in fact not considered into plant reliability models to inform system engineers on the most critical components. Currently, the propagation of quantitative health data from the component to the system level is a challenge given the diverse nature/structure of the data. On the other hand, plant reliability methods (which are typically based on fault-trees or reliability block diagrams) can effectively propagate data from the component to the system level, but values of failure rates or failure probabilities are an approximated integral representation of the past industry-wide operational experience, and it neglects the present component health status (e.g., diagnostic and condition-based data) and health projection (when available from prognostic data). Our first claim is that system reliability models should propagate health information from the component to the system/plant level in order to provide a quantitative snapshot of system/plant health and identify the most critical components. Our second claim is that component health should be informed solely by that specific component current and historical performance data and should not be an approximated integral representation of the past industry-wide operational experience. This paper is directly supporting these two claims by proposing a different approach to perform reliability modeling which relies on available component diagnostic, prognostic and condition-based data to measure component health, and it propagates this information through fault tree models. The propagation of health data from the component to the system level is performed not in terms of probability, but in terms of margins where margin is defined as the “distance” between the present actual status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated to a component performance, a margin-based approach focuses on the cause of an undesired component performance (i.e., component health). Hence, thinking of reliability in terms of margins implies decision making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical components.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Reliability modeling in a predictive maintenance context: A margin-based approach

Current system reliability methods (typically based on fault trees or reliability block diagrams) can effectively propagate reliability data from the asset to the system level in order to identify system critical points. However, employed asset reliability data are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models used to inform system engineers on the most critical assets. In addition, the propagation of quantitative health data from the asset to the system level is a challenge given the diverse nature and structure of health data elements (e.g., vibration spectra, temperature readings, expected failure time). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating available health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Here, this paper is directly addressing these two goals by proposing a different approach for reliability modeling that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. The propagation of health data from the asset to the system level is performed through fault tree models not in probability terms, but in terms of margin where margin is the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated with asset performance, a margin-based approach focuses on the cause of an undesired asset performance (i.e., its health). Hence, thinking of reliability in terms of margins implies decision-making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical assets.

97 - MATHEMATICS AND COMPUTING↗

NASA NTRS2007

Testing for Software Safety

This research focuses on testing whether or not the hazardous conditions identified by design-level fault tree analysis will occur in the target implementation. Part 1: Integrate fault tree models into functional specifications so as to identify testable interactions between intended behaviors and hazardous conditions. Part 2: Develop a test generator that produces not only functional tests but also safety tests for a target implementation in a cost-effective way. Part 3: Develop a testing environment for executing generated functional and safety tests and evaluating test results against expected behaviors or hazardous conditions. It includes a test harness as well as an environment simulation of external events and conditions.

Chen, Ken↗

DOE OSTI2018

Mining data in a dynamic PRA framework

Computational (also known as Dynamic) Probabilistic Risk Assessment (PRA) methods employs system simulation codes coupled with stochastic analysis tools in order to determine probabilities of certain outcomes such as system failure. In contrast to classical PRA methods (i.e., Event-Tree and Fault-Tree) in which timing and sequencing of events is set by the analyst, accident progression is dictated by the system control logic and its interaction with system temporal evolution. Due to the nature of the problem, Simulation-Based PRA methods require large amount of computationally expensive simulation runs. In addition, these methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). In this paper we investigate and apply several methods and algorithms to analyze these large amounts of time-dependent data. Furthermore, the scope of this article is to present a broad overview of methods and algorithms that can be used to improve data quality and to analyze and extract information from large data sets containing time dependent data. In this context, “extracting information” means constructing input-output correlations, finding commonalities, and identifying outliers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Improved Sampling Algorithms in the Risk-Informed Safety Margin Characterization Toolkit

The RISMC approach is developing advanced set of methodologies and algorithms in order to perform Probabilistic Risk Analyses (PRAs). In contrast to classical PRA methods, which are based on Event-Tree and Fault-Tree methods, the RISMC approach largely employs system simulator codes applied to stochastic analysis tools. The basic idea is to randomly perturb (by employing sampling algorithms) timing and sequencing of events and internal parameters of the system codes (i.e., uncertain parameters) in order to estimate stochastic parameters such as core damage probability. This approach applied to complex systems such as nuclear power plants requires to perform a series of computationally expensive simulation runs given a large set of uncertain parameters. These types of analysis are affected by two issues. Firstly, the space of the possible solutions (a.k.a., the issue space or the response surface) can be sampled only very sparsely, and this precludes the ability to fully analyze the impact of uncertainties on the system dynamics. Secondly, large amounts of data are generated and tools to generate knowledge from such data sets are not yet available. This report focuses on the first issue and in particular employs novel methods that optimize the information generated by the sampling process by sampling unexplored and risk-significant regions of the issue space: adaptive (smart) sampling algorithms. They infer system response from surrogate models constructed from existing samples and predict the most relevant location of the next sample. It is therefore possible to understand features of the issue space with a small number of carefully selected samples. In this report, we will present how it is possible to perform adaptive sampling using the RISMC toolkit and highlight the advantages compared to more classical sampling approaches such Monte-Carlo. We will employ RAVEN to perform such statistical analyses using both analytical cases but also another RISMC code: RELAP-7.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1991

Logic flowgraph methodology - A tool for modeling embedded systems

The logic flowgraph methodology (LFM), a method for modeling hardware in terms of its process parameters, has been extended to form an analytical tool for the analysis of integrated (hardware/software) embedded systems. In the software part of a given embedded system model, timing and the control flow among different software components are modeled by augmenting LFM with modified Petrinet structures. The objective of the use of such an augmented LFM model is to uncover possible errors and the potential for unanticipated software/hardware interactions. This is done by backtracking through the augmented LFM mode according to established procedures which allow the semiautomated construction of fault trees for any chosen state of the embedded system (top event). These fault trees, in turn, produce the possible combinations of lower-level states (events) that may lead to the top event.

Muthukumar, C. T.↗

NASA NTRS1993

A quantitative analysis of the F18 flight control system

This paper presents an informal quantitative analysis of the F18 flight control system (FCS). The analysis technique combines a coverage model with a fault tree model. To demonstrate the method's extensive capabilities, we replace the fault tree with a digraph model of the F18 FCS, the only model available to us. The substitution shows that while digraphs have primarily been used for qualitative analysis, they can also be used for quantitative analysis. Based on our assumptions and the particular failure rates assigned to the F18 FCS components, we show that coverage does have a significant effect on the system's reliability and thus it is important to include coverage in the reliability analysis.

Doyle, Stacy A.↗

DOE OSTI2014

The RISMC Approach to Perform Advanced PRA Analyses

The existing fleet of nuclear power plants is in the process of extending its lifetime and increasing the power generated from these plants via power uprates. In order to evaluate the impact of these two factors on the safety of the plant, the RISMC Pathway aims to develop simulation-based tools and methods to assess risks for existing nuclear power plants in order to optimize safety. This pathway, by developing new methods, is extending the state-of-the-practice methods that have been traditionally based on logic structures such as Event-Trees and Fault-Trees. These static types of models mimic system response in an inductive and deductive way respectively, yet are restrictive in the ways they can represent spatial and temporal constructs. RISMC analyses are performed by using a combination of thermal-hydraulic codes and a stochastic analysis tool currently under development at the Idaho National Laboratory, i.e. RAVEN. This paper presents a case study in order to show the capabilities of the RISMC methodology to assess impact of power uprate of a boiling water reactor system during a station blackout accident scenario. We employ the system simulator code, RELAP5-3D, coupled with RAVEN which perform the stochastic analysis. Our analysis is in fact performed by: 1) sampling values of a set of parameters from the uncertainty space of interest, 2) simulating the system behavior for that specific set of parameter values and 3) analyzing the set of simulation runs. Results obtained give a detailed investigation of the issues associated with a plant power uprate including the effects of station blackout accident scenarios. We are able to quantify how the timing of specific events was impacted by a higher nominal reactor core power. Such safety insights can provide useful information to the decision makers to perform risk informed margins management.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2011

SAPHIRE 8 Volume 2 - Technical Reference

The Systems Analysis Programs for Hands-on Integrated Reliability Evaluations (SAPHIRE) refers to a set of computer programs that were developed to create and analyze probabilistic risk assessment (PRAs). Herein information is provided on the principles used in the construction and operation of Version 8.0 of the SAPHIRE system. This report summarizes the fundamental mathematical concepts of sets and logic, fault trees, and probability. This volume then describes the algorithms used to construct a fault tree and to obtain the minimal cut sets. It gives the formulas used to obtain the probability of the top event from the minimal cut sets, and the formulas for probabilities that apply for various assumptions concerning reparability and mission time. It defines the measures of basic event importance that SAPHIRE can calculate. This volume gives an overview of uncertainty analysis using simple Monte Carlo sampling or Latin Hypercube sampling, and states the algorithms used by this program to generate random basic event probabilities from various distributions. Also covered are enhance capabilities such as seismic analysis, Workspace algorithms, cut set "recovery," end state manipulation, and use of "compound events."

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Risk assessment of safety-critical data communication in digital safety feature control system - 205

As one of the safety-critical systems in Advanced Power Reactor-1400 (APR-1400) nuclear power plant (NPP), the Engineered Safety Feature-Component Control System (ESF-CCS) employs safety data link and network communication for transmitting safety component actuation to effectively facilitate various safety-critical field controllers. However, data communication failure risk in the ESF-CCS has yet to be fully quantified, the probability that a safety-critical system in digitalized NPP becomes unsafe due to a network failure must be evaluated to quantify the risk of digital I and C system. In this study, a fault tree model was developed to assess the data link and data network failure-induced unavailability of a safety-critical system function used to generate an automated control signal for actuating design basis accident mitigation equipment. Especially, the risk of data communication failure in a digital safety feature control system was analyzed in consideration of interconnection between safety-critical controllers and the fault-tolerant algorithm implemented in the network configuration. Based on the developed fault tree model, case studies were performed to quantitatively assess the unavailability of ESF-CCS signal generation due to safety data link and network failure and their risk effect on safety signal generation unavailability. This study is expected to provide risk information on the safety-critical data communication in a digitalized NPP instrumentation and control system. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS2005

Monte Carlo Simulation of Markov, Semi-Markov, and Generalized Semi- Markov Processes in Probabilistic Risk Assessment

A standard tool of reliability analysis used at NASA-JSC is the event tree. An event tree is simply a probability tree, with the probabilities determining the next step through the tree specified at each node. The nodal probabilities are determined by a reliability study of the physical system at work for a particular node. The reliability study performed at a node is typically referred to as a fault tree analysis, with the potential of a fault tree existing.for each node on the event tree. When examining an event tree it is obvious why the event tree/fault tree approach has been adopted. Typical event trees are quite complex in nature, and the event tree/fault tree approach provides a systematic and organized approach to reliability analysis. The purpose of this study was two fold. Firstly, we wanted to explore the possibility that a semi-Markov process can create dependencies between sojourn times (the times it takes to transition from one state to the next) that can decrease the uncertainty when estimating time to failures. Using a generalized semi-Markov model, we studied a four element reliability model and were able to demonstrate such sojourn time dependencies. Secondly, we wanted to study the use of semi-Markov processes to introduce a time variable into the event tree diagrams that are commonly developed in PRA (Probabilistic Risk Assessment) analyses. Event tree end states which change with time are more representative of failure scenarios than are the usual static probability-derived end states.

English, Thomas↗