An intelligent system by fuzzy reliability algorithm in fault tree analysis for nuclear power plant probabilistic safety assessment

© Imperial College Press. Fault tree analysis for nuclear power plant probabilistic safety assessment is an intricate process. Personal computer-based software systems have therefore been developed to conduct this analysis. However, all existing fault tree analysis software systems only accept quantitative data to characterized basic event reliabilities. In real-world applications, basic event reliabilities may not be represented by quantitative data but by qualitative justifications. The motivation of this work is to develop an intelligent system by fuzzy reliability algorithm in fault tree analysis, which can accept not only quantitative data but also qualitative information to characterized reliabilities of basic events. In this paper, a newly-developed system called InFaTAS-NuSA is presented and its main features and capabilities are discussed. To benchmark the applicability of the intelligent concept implemented in InFaTAS-NuSA, a case study is performed and the analysis results are compared to the results obtained from a well-known fault tree analysis software package. The results confirm that the intelligent concept implemented in InFaTAS-NuSA can be very useful to complement conventional fault tree analysis software systems

Framework, approach and system of intelligent fault tree analysis for nuclear safety assessment

University of Technology, Sydney. Faculty of Engineering and Information Technology.Probabilistic safety assessment by fault tree analysis has been considered as an important tool to evaluate safety systems of nuclear power plants in the last two decades. However, since the estimation of failure probabilities of rare events with high consequences is the focus of this assessment, it is often very difficult to obtain component failure rates, which are specific to the nuclear power plant under evaluation. The motivation of this study is how to obtain basic event failure rates when basic events do not have historical failure data and expert subjective justifications, which are expressed in qualitative failure possibilities, are the only means to evaluate basic event failures. This thesis describes a new intelligent hybrid fault tree analysis framework to overcome the weaknesses of conventional fault tree analysis, qualitative failure possibilities and their corresponding mathematical representations to articulate nuclear event failure likelihoods, an area defuzzification technique to decode the membership functions of fuzzy sets representing nuclear event failure possibilities into nuclear event reliability scores, and a fuzzy reliability approach to generate nuclear event quantitative fuzzy failure rates from the corresponding qualitative failure possibilities subjectively evaluated by experts. Seven qualitative linguistic terms have been defined to represent nuclear event failure possibilities, i.e. very low, low, reasonably low, moderate, reasonably high, high, and very high and the corresponding mathematical forms are represented by triangular fuzzy numbers, which are defined in the [0, 1] universe of discourse based on nuclear event failure data documented in literatures using inductive reasoning. Finally, an intelligent software system called InFaTAS-NuSA, which has been developed to realize the new intelligence hybrid fault tree analysis framework to overcome the limitations of the existing fault tree analysis software systems by accepting both quantitative failure probabilities and qualitative failure possibilities, is also described in this thesis. The results of the InFaTAS-NuSA evaluation using a real world application confirm that InFaTAS-NuSA has yielded similar outputs as the outputs generated by a well-known fault tree analysis software system, i.e. SAPHIRE, and therefore it can overcome the limitation of the existing fault tree analysis software system, which can accept only quantitative failure probabilities. The experiment results also show that the fuzzy reliability approach seems to be a sound alternative for conventional reliability approach to deal with basic events which do not have historical failure data and expert subjective opinions are the only means to obtain their failure information

Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review

Human factors and ergonomics have played an essential role in increasing the safety and performance of operators in the nuclear energy industry. In this critical review, we examine how artificial intelligence (AI) technologies can be leveraged to mitigate human errors, thereby improving the safety and performance of operators in nuclear power plants (NPPs). First, we discuss the various causes of human errors in NPPs. Next, we examine the ways in which AI has been introduced to and incorporated into different types of operator support systems to mitigate these human errors. We specifically examine (1) operator support systems, including decision support systems, (2) sensor fault detection systems, (3) operation validation systems, (4) operator monitoring systems, (5) autonomous control systems, (6) predictive maintenance systems, (7) automated text analysis systems, and (8) safety assessment systems. Finally, we provide some of the shortcomings of the existing AI technologies and discuss the challenges still ahead for their further adoption and implementation to provide future research directions

Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded

INTEGRATED DETERMINISTIC AND PROBABILISTIC SAFETY ANALYSIS: CONCEPTS, CHALLENGES, RESEARCH DIRECTIONS

International audienceIntegrated deterministic and probabilistic safety analysis (IDPSA) is conceived as a way to analyze the evolution of accident scenarios in complex dynamic systems, like nuclear, aerospace and process ones, accounting for the mutual interactions between the failure and recovery of system components, the evolving physical processes, the control and operator actions, the software and firmware. In spite of the potential offered by IDPSA, several challenges need to be effectively addressed for its development and practical deployment. In this paper, we give an overview of these and discuss the related implications in terms of research perspectives

The Implementation of Importance Measure Approaches for Criticality Analysis in Fault Tree Analysis: A Review

THE IMPLEMENTATION OF IMPORTANCE MEASURE APPROACHES FOR CRITICALITY ANALYSIS IN FAULT TREE ANALYSIS: A REVIEW.Fault tree analysis (FTA) has been widely applied in nuclear power plant (NPP) probabilistic safety assessment to evaluate the reliability of a safety system. In FTA, criticality analysis is performed to identify the weakest paths in the system designs and components. For this purpose, an importance measure approach can be applied. Risk managers can apply information obtained from this analysis to improve safety by implementing risk reduction measure into the new design or build a more innovative design. Various importance measure approaches have been developed and proposed for criticality analysis in FTA. Each important measure approach offers specific purposes and advantages but has limitations. Therefore, it is necessary to understand characteristics of each approach in order to select the most appropriate approach to reach the purpose of the study. The objective of this study is to review the current implementations of importance measure approaches to rank individual basic events and/or minimal cut sets regarding their contributions to the unreliability or unavailability of NPP safety systems. This study classified importance measure approaches into two groups, i.e. probability–based importance measure approaches and fuzzy–based importance measure approaches. This study concluded that clear understanding of the purpose of the study, the type of reliability data at hands, and the uncertainty in the calculation need to be considered prior to the selection of the appropriate importance measure approach to the study of interest.

A review of applications of fuzzy sets to safety and reliability engineering

Safety and reliability are rigorously assessed during the design of dependable systems. Probabilistic risk assessment (PRA) processes are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). In conventional PRA, failure data about components is required for the purposes of quantitative analysis. In practice, it is not always possible to fully obtain this data due to unavailability of primary observations and consequent scarcity of statistical data about the failure of components. To handle such situations, fuzzy set theory has been successfully used in novel PRA approaches for safety and reliability evaluation under conditions of uncertainty. This paper presents a review of fuzzy set theory based methodologies applied to safety and reliability engineering, which include fuzzy FTA, fuzzy FMEA, fuzzy ETA, fuzzy Bayesian networks, fuzzy Markov chains, and fuzzy Petri nets. Firstly, we describe relevant fundamentals of fuzzy set theory and then we review applications of fuzzy set theory to system safety and reliability analysis. The review shows the context in which each technique may be more appropriate and highlights the overall potential usefulness of fuzzy set theory in addressing uncertainty in safety and reliability engineering

Fuzzy Human Reliability Analysis: Applications and Contributions Review

The applications and contributions of fuzzy set theory to human reliability analysis (HRA) are reassessed. The main contribution of fuzzy mathematics relies on its ability to represent vague information. Many HRA authors have made contributions developing new models, introducing fuzzy quantification methodologies. Conversely, others have drawn on fuzzy techniques or methodologies for quantifying already existing models. Fuzzy contributions improve HRA in five main aspects: (1) uncertainty treatment, (2) expert judgment data treatment, (3) fuzzy fault trees, (4) performance shaping factors, and (5) human behaviour model. Finally, recent fuzzy applications and new trends in fuzzy HRA are herein discussed