Structural reliability prediction of a steel bridge element using dynamic object oriented Bayesian Network (DOOBN)

Different from conventional methods for structural reliability evaluation, such as, first/second-order reliability methods (FORM/SORM) or Monte Carlo simulation based on corresponding limit state functions, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper. The DOOBN approach can effectively model the deterioration processes of a steel bridge element and predict their structural reliability over time. This approach is also able to achieve Bayesian updating with observed information from measurements, monitoring and visual inspection. Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system. A steel bridge girder is used to validate the proposed approach. The predicted results are compared with those evaluated by FORM method

Modelling complex large scale systems using object oriented Bayesian networks (OOBN)

The aim of this communication is to present a new way of how to structure modelling process of complex and large scale systems by object oriented Bayesian network (OOBN) for risk assessment and management purpose. In the first stage, we extend OOBN by presenting a new definition that introduces some flexibility, in a second stage, dynamic Bayesian networks (DBN) described by OOBN method are presented, that leads to a framework that we refer to as Dynamic Objet Oriented Bayesian Network (DOOBN). A demonstration in the domain of risk assessment of flash floods effect on the infrastructures inoperability is considered to show potential applicability of the extended OOBN

Methodological developments for probabilistic risk analyses of socio-technical systems

International audienceNowadays, the risk analysis of critical systems cannot be focused only on a technical point of view. Indeed, several major accidents have changed this initial way of thinking. As a result, there exist numerous methods that allow to study risks by considering on the main system resources: the technical process, the operator constraining this process, and the organisation conditioning human actions. However, few works propose to jointly use these different methods to study risks in a global approach. In that way, this paper presents a methodology, which is under development between CRAN, EDF and INERIS, allowing an integration of these different methods to probabilistically estimate risks. This integration is based on unification and structuring knowledge concepts; and the quantitative aspect is achieved through the use of Bayesian Networks. An application of this methodology, on an industrial case, demonstrates its feasibility and concludes on model capacities, which are about the necessary consideration of the whole causes for a system weakness treatment, and the classification of these contributors considering their criticality for this system. This tool can thus be used to help decision makers to prioritise their actions

Bayesian Network Modelling the risk analysis of complex socio technical systems

International audienceThe risk analysis of a system is a multidisciplinary process in constant evolution. Indeed, if a few years ago, analyses were limited at the technical level, it is today necessary to consider the system in a global way, by including Human beings and Organisations. But this involves an increasing complexity of the studied system, because of the widening of its limits and the diversity of considered disciplines. This article proposes a method to structure the knowledge in a decisionmaking model

ОПТИМІЗАЦІЇ РЕЗЕРВУ ОБЛАДНАННЯ ДЛЯ ІНТЕЛЕКТУАЛЬНИХ АВТОМАТИЗОВАНИХ СИСТЕМ

Algorithms for a neural network analyzer involved in the decision support system (DSS) during the selection of the composition of backup equipment (CBE) for intelligent automated control systems Smart City are proposed. A model, algorithms and software have been developed for solving the optimization problem of choosing a CBE capable of ensuring the uninterrupted operation of the IACS both in conditions of technological failures and in conditions of destructive interference in the operation of the IACS by the attackers. The proposed solutions help to reduce the cost of determining the optimal CBE for IACS by 15–17% in comparison with the results of known calculation methods. The results of computational experiments to study the degree of influence of the outputs of the neural network analyzer on the efficiency of the functioning of the CBE for IACS are presented.Запропоновано алгоритми для нейромережевого аналізатора, задіяного у системі підтримки прийняття рішень (СППР) у ході вибору складу резервного обладнання (СРО) для інтелектуальних автоматизованих систем управління (ІАСУ) Smart City. Розроблено модель, алгоритми та відповідне програмне забезпечення для вирішення оптимізаційного завдання вибору СРО, здатного забезпечити безперебійну роботу ІАСУ як в умовах технологічних збоїв, так і в умовах деструктивного втручання у роботу ІАСУ з боку атакуючих. Запропоновані рішення сприяють скороченню витрат на визначення оптимального СРО для ІАС на 15–17% порівняно з результатами відомих методів розрахунку. Наведено результати обчислювальних експериментів для вивчення ступеня впливу кількості виходів нейромережевого аналізатора на ефективність функціонування СРО для ІАС

Modelling a manufacturing line using extended object oriented bayesian network

Bayesian Network (BN) is a widely used modelling tool in probabilistic reasoning; however it turns out to be difficult to use this tool to model a large scale complex system such as a manufacturing line due to the number of parameters when the system exceeds a certain amount of components. Motivated by the necessity to both reduce the complexity of the model while increasing the capacity of integrating a large number of parameters, this communication ambitions to propose a new modelling approach, called Extended Object Oriented Bayesian Network (EOOBN). The EOOBN is an underlying mathematical tool which has much more flexibility than classical Bayesian Networks. The main aim of the communication is then to present a methodology dedicated to EOOBN construction. After having introduced the main concepts and described the EOOBN building principles, an industrial application is proposed to illustrate the developments

Applications of Bayesian networks in chemical and process industries: A review

Despite technological advancements, chemical and process industries are still prone to accidents due to their complexity and hazardous installations. These accidents lead to significant losses that represent economic losses and most importantly human losses. Risk management is one of the appropriate tools to guarantee the safe operations of these plants. Risk analysis is an important part of risk management, it consists of different methods such as Fault tree, Bow-tie, and Bayesian network. The latter has been widely applied for risk analysis purposes due to its flexible and dynamic structure. Bayesian networks approaches have shown a significant increase in their application as shown by in the publication in this field. This paper summarizes the result of a literature review performed on Bayesian network approaches adopted to conduct risk assessments, safety and risk analyses. Different application domains are analysed (i.e. accident modelling, maintenance area, fault diagnosis) in chemical and process industries from the year 2006 to 2018. Furthermore, the advantages of different types of Bayesian networks are presented

Modelling a large scale system for risk assessment

The aim of this communication is to present an earlier study of how to structure modelling process of complex and large scale systems for risk assessment and management purpose. The approach, in a first stage, uses ontology paradigm to determine variables (or concepts) characterizing system locally and the nature of relationships relating them and, in a second stage, object oriented Bayesian network (OOBN) to characterize the strength of these relationships in terms of conditional probabilities tables given that one of main feature of complex systems is the uncertainty that affect the relationships between different variables. A case study in the domain of risk assessment of flash floods effect on the infrastructures inoperability is considered to show potential applicability of the developed approach

Expert Elicitation for Reliable System Design

This paper reviews the role of expert judgement to support reliability assessments within the systems engineering design process. Generic design processes are described to give the context and a discussion is given about the nature of the reliability assessments required in the different systems engineering phases. It is argued that, as far as meeting reliability requirements is concerned, the whole design process is more akin to a statistical control process than to a straightforward statistical problem of assessing an unknown distribution. This leads to features of the expert judgement problem in the design context which are substantially different from those seen, for example, in risk assessment. In particular, the role of experts in problem structuring and in developing failure mitigation options is much more prominent, and there is a need to take into account the reliability potential for future mitigation measures downstream in the system life cycle. An overview is given of the stakeholders typically involved in large scale systems engineering design projects, and this is used to argue the need for methods that expose potential judgemental biases in order to generate analyses that can be said to provide rational consensus about uncertainties. Finally, a number of key points are developed with the aim of moving toward a framework that provides a holistic method for tracking reliability assessment through the design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287], [arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modélisation graphique probabiliste pour la maîtrise des risques, la fiabilité et la synthèse de lois de commande des systèmes complexes

Mes travaux de recherche sont menés au Centre de Recherche en Automatique de Nancy (CRAN), dans le département Ingénierie des Systèmes Eco-Techniques (ISET) sous la responsabilité de B. Iung et de A. Thomas et le département Contrôle - Identification - Diagnostic (CID) sous la responsabilité de D. Maquin et de G. Millerioux.L’objectif principal de mes recherches est de formaliser des méthodes de construction de modèles probabilistes représentant les bons fonctionnements et les dysfonctionnements d’un système industriel. Ces modèles ont pour but de permettre l’évaluation des objectifs de fonctionnement du système (exigences opérationnelles, performances) et les conséquences en termes de fiabilité et de maîtrise des risques (exigences de sûreté). Ceci nécessite de modéliser les impacts de l’environnement sur le système et sur ses performances, mais aussi l’impact des stratégies de commande et des stratégies de maintenance sur l’état de santé du système.Pour plus de détails.A travers les différents travaux de thèses et collaborations, j’ai exploité différents formalismes de modélisation probabilistes. Les apports majeurs de nos contributions se déclinent en 3 points :• La modélisation des conséquences fonctionnelles des défaillances, structurée à partir des connaissances métiers. Nous avons développés les principes de modélisation par Réseau Bayésien (RB) permettant de relier la fiabilité et les effets des états de dégradation des composants à l’architecture fonctionnelle du système. Les composants et les modes de défaillances sont alors décrits naturellement par des variables multi-états ce qui est difficile à modéliser par les méthodes classiques de sûreté de fonctionnement. Nous proposons de représenter le modèle selon différents niveaux d'abstraction en relation avec l’analyse fonctionnelle. La modélisation par un modèle probabiliste relationnel (PRM) permet de capitaliser la connaissance par la création des classes génériques instanciées sur un système avec le principe des composants pris sur étagère.• Une modélisation dynamique de la fiabilité des systèmes pris dans leur environnement. Nous avons contribué lors de notre collaboration avec Bayesia à la modélisation de la fiabilité des systèmes par Réseau Bayésien Dynamique (RBD). Un RBD permet, grâce à la factorisation de la loi jointe, une complexité inférieure à une Chaîne de Markov ainsi qu’un paramétrage plus facile. La collaboration avec Bayesia a permis l’intégration dans Bayesialab (outil de modélisation) de ces extensions et notamment l’utilisation de paramètres variables dans le temps élargissant la modélisation des RBD à des processus Markoviens non homogènes.• La synthèse de la loi de commande pour l’optimisation de la fiabilité du système. Nous travaillons sur l’intégration de la fiabilité dans les objectifs de commande des systèmes sous contrainte de défaillances ou de défauts. Nous posons aujourd’hui le problème dans un contexte général de commande. Nous proposons une structuration du système de commande intégrant des fonctions d’optimisation et des fonctions d’évaluation de grandeurs probabilistes liées à la fiabilité du système. Nos travaux récents sont focalisés sur l’intégration, dans la boucle d’optimisation de la commande, des facteurs issues d’une analyse de sensibilité de la fiabilité du système par rapport aux composants