Dynamic reliability analysis framework using fault tree and dynamic Bayesian network: A case study of NPP

This report presents an enhanced performance evaluation of emergency power system (EPS) and high-pressure core spray (HPCS) emergency diesel generators (EDGs) at U.S. commercial nuclear power plants. This report evaluates component performance over time using (1) Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS) data from 1998 through 2020 and (2) maintenance unavailability performance data from Mitigating Systems Performance Index (MSPI) Basis Document data from 2002 through 2020. The objective is to show estimates of current failure probabilities and rates related to EDGs, trend these data on an annual basis, determine if the current data are consistent with the probability distributions currently recommended for use in NRC probabilistic risk assessments, show how the reliability data differ for different EDG manufacturers and for EDGs with different ratings; and summarize the subcomponents, causes, detection methods, and recovery associated with each EDG failure mode. The EDG failure modes considered are fail to start (FTS), fail to load and run (FTLR), and fail to run after one hour of operation (FTR>1H). Engineering analyses were performed with respect to time period and failure mode without regard to the actual number of EDGs at each plant. The factors analyzed are: subcomponent, failure cause, detection method, recovery, manufacturer, and EDG rating. The following increasing trends were identified for EDGs for the most recent 10-year period: o HPCS EDG FTS failure probability o EPS and HPCS EDG frequency of start demands (demands per reactor year) o EPS and HPCS EDG frequency of FTLR demands. The following decreasing trends were identified for EDGs for the most recent 10-year period: o EPS EDG FTS failure probability o EPS EDG FTLR failure probability o EPS EDG unavailability o EPS and HPCS EDG frequency of FTLR events (failures per reactor year).

Referring to the different types of periodic test of the whole Emergency Diesel Generator system and considering the features of the digitalized protection system used in Emergency Diesel Generator of nuclear power plant, also on the basis of reliability analysis, this paper firstly describes the periodic test of the protection system, then introduces the design requirement of the Emergency Diesel Generator protection system and finally figures out a design scheme. This scheme provides technical reference for the subsequent digitalized modernization of Emergency Diesel Generator protection system periodic test scheme design.KeywordsEmergency diesel generatorProtection systemPeriodic testDigitalized transformation

The current approach to evaluating the risk of nuclear power plant (NPP) operation relies on static probabilities of component failure, which are based on industry experience with the existing fleet of nominally similar light water reactors (LWRs). As the nuclear industry looks to advanced reactor designs that feature non-light water coolants (e.g., liquid metal, high temperature gas, molten salt), this operating history is not available. Many advanced reactor designs use advanced components, such as electromagnetic pumps, that have not been used in the US commercial nuclear fleet. Given the lack of rich operating experience, we cannot accurately estimate the evolving probability of failure for basic components to populate the fault trees and event trees that typically comprise probabilistic risk assessment (PRA) models. Online equipment prognostics and health management (PHM) technologies can bridge this gap to estimate the failure probabilities for components under operation. The enhanced risk monitor (ERM) incorporates equipment condition assessment into the existing PRA and risk monitor framework to provide accurate and timely estimates of operational risk.

We propose a dynamic differential Bayesian networks (DDBNs) and nonparametric regression model. This model is an extended model of traditional dynamic Bayesian networks (DBNs), which can incorporate temporal information in a natural way and directly handle real-valued data obtained from microarrays without any transformation. In addition, it can cope with differential information between gene expression levels, without any loss to the traditional advantage, i.e., the capability of estimating non-linear relationships between genes. We apply DDBNs to analyze simulated data and real data, i.e., Saccharomyces cerevisiae cell cycle gene expression data. We have confirmed the effectiveness of our approach in the sense that some edges have been successfully detected only by DDBNs, not by DBNs.

Nowadays, several components and systems are designed and produced based on reliability. Since the reliability criterion has an important role in purchasing and implementation of these systems. In the design of a reliable system, fault and failure analysis must be carried out in order to reduce fault probability of the system. When dependency and the relation between components of a complex system are important and should be mentioned, determination of system reliability is very difficult. In this paper, dynamic fault tree is used to evaluate the systems reliability that their behavior is varied with time. Dynamic fault tree is constructed and then it converted to dynamic bayesian network. In this paper, the principle of dynamic fault tree gates and their mapping into dynamic bayesian are explained and some new relations between events and gates for this mapping are proposed. GeNIe package is used to determine dynamic bayesian network based on stochastic sampling algorithms. Four systems (cardiac assist system, hypothetical cascaded priority-and system, inertial navigation system/ global positioning system integrated, and emergency detection system) are investigated; reliability and fault probability of these systems are calculated. Comparison of the results with those obtained by other researches shows the proposed method effectiveness for systems reliability modeling and assessment via dynamic bayesian network.

In order to fully grasp the law of startup failure and operation failure of emergency diesel generator in nuclear power plants, this paper first presents the development method of integrated fault model for emergency diesel generator startup failure and operation failure on the basis of fully integrating the existing mature fault tree analysis methods. Then, taking a certain type of Emergency diesel generator as an example, a complete startup failure and operation failure model was developed, and qualitative analysis and quantitative evaluation were carried out on the basis of the model, which verified the accuracy and applicability of the model development method. Finally, this paper puts forward the qualitative analysis and quantitative evaluation results based on model, which are directly used in the basic application scheme of equipment safeguard measures and the advanced application scheme of dynamic risk assessment and long-term trend monitoring of emergency diesel generator sets based on model + data driven by emergency diesel generator sets. Through the advanced application solution, users can identify dynamic high-risk defects and long-term reliability management weaknesses in advance, and formulate effective short-term fault elimination and long-term fault prevention measures in time to avoid the consequences of startup failure and operation failure. The above research results have important practical significance for the continuous improvement of the startup reliability and operation reliability of emergency diesel generator in nuclear power plants.

Development of a quantitative method for evaluating the efficacy of cyber security controls in NPPs based on intrusion tolerant concept

The unreliability evaluation of a system including dependencies involving the state of components or the failure events, can be performed by modelling the system as a dynamic fault tree (DFT). The combinatorial technique used to solve standard Fault Trees is not suitable for the analysis of a DFT. The conversion into a dynamic Bayesian network (DBN) is a way to analyze a DFT. This paper presents a software tool allowing the automatic analysis of a DFT exploiting its conversion to a DBN. First, the architecture of the tool is described, together with the rules implemented in the tool, to convert dynamic gates in DBNs. Then, the tool is tested on a case of system: its DFT model and the corresponding DBN are provided and analyzed by means of the tool. The obtained unreliability results are compared with those returned by other tools, in order to verify their correctness. Moreover, the use of DBNs allows to compute further results on the model, such as diagnostic and sensitivity indices

While offshore salt caverns, as part of the carbon capture and storage (CCS) systems, could offer significant advantages in storage carbon dioxide, they are still not widely implemented, partially due the challenges in maintaining the safe operation of such systems with a long-projected storage time. The presented study provided a Fault Tree (FT) analysis for the causal relationships of storage system failures with respect to the root causes, and further the potential integration of the FT with the dynamic Bayesian network (DBN) in order to monitor offshore salt caverns in the long-term operations. The presented study can incorporate both physics-of-failure models and sensory data to update the failure probabilities of the components in the offshore salt cavern storage system. The fault tree considers unintentional flow as the main failure mode and divides the system into three subsystems: the wellhead, the well and the cavern itself. Then, each individual subsystem is studied based on the components in the subsystem and the NORSOK standards for safe well design. The fault tree presented in this study also outlines the relationship between the failure modes and the effect they have on the system failure. Th fault tree analysis tool can then be converted into a DBN to provide a long-term monitoring framework for offshore CCS systems with limited observability, where real-time data can be utilized in order to update the probabilities of failure of components over time.

Uncertainty in fault tree analysis: A fuzzy approach

This article proposes a cyber-security improvement plan for the DCS control system of nuclear power plants. Firstly, a brief introduction was given to the traditional architecture of DCS control systems of nuclear power plants. Secondly, the current cyber-security risks faced by DCS control systems in nuclear power plants were summarized, including identity authentication issues, access control issues, communication protocol issues, and data storage issues. Based on the cyber-security risks faced by the DCS control system in nuclear power plants, an improvement plan has been proposed for the DCS platform without increasing external cyber-security equipment. Firstly, regarding identity authentication, it is proposed that nuclear power plants have a very good physical protection that can effectively protect the operator station. However, for the engineer station, a dual factor identity authentication method needs to be added. Secondly, regarding access control, the shortcomings of role-based access control models were pointed out, and a location-based and task-based access control model was proposed. Then, in terms of security communication networks, the sensitive data range of nuclear power plants was analyzed, and communication was divided into internal communication and external communication. For internal communication, pre-stored keys can be used for encrypted communication. For external communication, certificates should be used for identity authentication, and then encrypted communication should be carried out. Finally, for data integrity, control system configurations can be saved through encryption. For passwords, they can be transmitted and saved through hash values.

Model extension and improvement for simulator-based software safety analysis

The article discusses a method for analyzing the risk assessment of failures of the structure as a system by developing a "Fault Tree Analysis". Formulas were proposed for the numerical estimation of the probability of system failure, taking into account the stochastic dependence of the failures of its elements. On the example of a nuclear power facility, a "wet" stand-alone storage facility for spent nuclear fuel, an analysis of possible scenarios of a facility failure was carried out, a "Fault Tree Analysis" was developed and calculated under seismic impact. It has been determined that the risk of structure as a
 system under seismic impact is determined by the risk of falling process
 equipment and building structures on the overlap of storage compartments or
 stored nuclear fuel. To increase the safety of building structures under
 seismic impact, it is necessary to pay special attention to the design features
 of the frame part and the interface between the monolithic storage compartment
 and the frame part, as the most vulnerable link.

As the development of computer science technology and the requirement of thorough research, more researchers are setting their eyes on coupling method because of the tight coupling in NPP (nuclear power plant) system. However there were few researches studying the difference between decoupling and coupling methods and the importance of coupling method. This research respectively establishes the primary and secondary loop decoupling models and the two loops coupling model based on the small NPP by APROS. Then the differences between the decoupling and coupling models is studied under the steady state and dynamic state which contains the ramp load variation and load shedding. The results show that there are small differences between these models in the main parameter values under the steady state. But the differences between decoupling models and coupling model are large. Therefore the NPP system needs be modeled by coupling method as to study its dynamic characteristic.

This paper presents a comparative study of availability evaluation of systems modeled by a mixed fault tree (MFT). This MFT contain a static sub-trees and dynamic sub-trees. These sub-trees can be independent or dependent. To evaluate the availability of a system from its fault tree, three methods are applied here: the first uses the classic Bayesian networks (CBN) for static sub-trees and dynamic Bayesian networks (DBN) for dynamic sub-trees, the second being proposed, bases on the binary decision diagrams (BDD) for static sub-trees and dynamic Bayesian networks (DBN) for dynamic sub-trees and the third being proposed uses the binary decision diagram (BDD) for static sub-trees and Markov chains (MC) for dynamic sub-trees . The three methods are compared in both cases; independence and dependence of sub-trees.