An integrated framework for condition-informed probabilistic risk assessment

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An integrated framework for business continuity management of critical infrastructures

International audience Business continuity of critical infrastructures (CIs) is exposed to various hazards including random failures, malicious threats, natural disasters and human errors, which could generate accidents with serious consequences (fatality, injury, environmental damage, business interruption and company reputation loss). We conceptualize the business continuity management (BCM) process as the integration of four active stages: prevention, mitigation, emergency and recovery. Integrated assessment and management is needed on all stages. On the contrary, the current approaches of BCM have not considered all phases in an integrated man-ner. We propose a new framework, which stands on an extension of the Bow-Tie model, to efficiently and effectively prevent and mitigate the potential consequences of an accident by properly designing and strengthening safety barriers for preventing and mitigating accidents, and making safety decisions for emer-gency and recovery. The proposed framework allows considering safety barriers and decisions in an integrated way. For operationalization, we explore the use of two complementary quantitative methods, Bayesian Network (BN) and Constraint Goal Method. BN takes the negative viewpoint of failure to determine the causes which lead to the final damage. CGM employs the positive perspective of the goal achievement process. An oil pipeline system is considered to show the application of the proposed approaches. Document type: Part of book or chapter of boo

Un cadre quantitatif pour l'évaluation et l'optimisation dynamique de la continuité d'activité des systèmes énergétique

Business continuity management is a comprehensive framework to prevent the disruptive events from impacting the business operations, quickly recovering business and reducing the corresponding potential damages for energy system, such as nuclear power plants (NPPs). This dissertation provides discussions on the following aspects: developing appropriate risk assessment methods in order to integrate condition monitoring data and inspection data for a robust and real-time risk profile updating and prognostics. To account for the uncertainty of condition monitoring data, a hidden Markov gaussian mixture model is developed to model the condition monitoring data. A Bayesian network is applied to integrate the two data sources. For improving applicability of business continuity in practice, time-variant variables regard business continuity index, e.g. component degradation, time-dependent revenue, etc are taken into consideration in the business continuity modelling process. Based on the proposed business continuity index, a joint optimization method considering all the safety measures in event evolvement process including prevention stage, mitigation stage, emergency stage and recovery stage is developed to enhance system business continuity under limited resources. The proposed methodologies are applied to NPP against disruptive event.La gestion de la continuité des opérations est un cadre complet visant à éviter que les événements perturbateurs n’affectent les opérations commerciales, à rétablir rapidement les activités et à réduire les dommages potentiels correspondants pour les systèmes énergétiques, tels que les centrales nucléaires. Cette thèse propose des discussions sur les aspects suivants: développement de méthodes appropriées d'évaluation des risques afin d'intégrer les données de surveillance de l'état et les données d'inspection pour une mise à jour et des pronostics robustes et en temps réel du profil de risque. Pour tenir compte de l'incertitude des données de surveillance de l'état, un modèle de mélange gaussien de Markov caché est développé pour modéliser les données de surveillance de l'état. Un réseau bayésien est appliqué pour intégrer les deux sources de données. Pour améliorer l'applicabilité de la continuité des opérations dans la pratique, les variables variant dans le temps considèrent l'indice de continuité des opérations, par ex. la dégradation des composants, les revenus en fonction du temps, etc. sont pris en compte dans le processus de modélisation de la continuité des activités. Sur la base de l'indice de continuité d'activité proposé, une méthode d'optimisation conjointe prenant en compte toutes les mesures de sécurité dans le processus d'évolution des événements, y compris les étapes de prévention, d'atténuation, d'urgence et de récupération, est développée pour améliorer la continuité des opérations du système avec des ressources limitées. Les méthodologies proposées sont appliquées aux centrales nucléaires contre les événements perturbateurs

An integrated framework for business continuity management of critical infrastructures

none2noBusiness continuity of critical infrastructures (CIs) is exposed to various hazards including random failures, malicious threats, natural disasters and human errors, which could generate accidents with serious consequences (fatality, injury, environmental damage, business interruption and company reputation loss). We conceptualize the business continuity management (BCM) process as the integration of four active stages: prevention, mitigation, emergency and recovery. Integrated assessment and management is needed on all stages. On the contrary, the current approaches of BCM have not considered all phases in an integrated manner. We propose a new framework, which stands on an extension of the Bow-Tie model, to efficiently and effectively prevent and mitigate the potential consequences of an accident by properly designing and strengthening safety barriers for preventing and mitigating accidents, and making safety decisions for emergency and recovery. The proposed framework allows considering safety barriers and decisions in an integrated way. For operationalization, we explore the use of two complementary quantitative methods, Bayesian Network (BN) and Constraint Goal Method (CGM). BN takes the “negative” viewpoint of failure to determine the causes which lead to the final damage. CGM employs the positive perspective of the goal achievement process. An oil pipeline system is considered to show the application of the proposed approaches.Xing, J.; Zio, E.Xing, J.; Zio, Enric

Joint optimization of safety barriers for enhancing business continuity of nuclear power plants against steam generator tube ruptures accidents

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A hybrid method for prognostics of lithium-ion batteries capacity considering regeneration phenomena

International audiencePrognostics and health management (PHM) is crucial to the reliability and safety of lithium-ion batteries. In this respect, the capacity regeneration phenomenon that occurs during the process of battery degradation brings a challenge to the accuracy of capacity prediction. In this paper, a hybrid method is proposed for the accurate prediction of lithium-ion batteries capacity considering regeneration. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to decompose the raw capacity signal into the global degradation trend components and the local fluctuation components. Then, each component is separately fed to the adaptive neuro-fuzzy inference system (ANFIS) for prediction. Finally, the individual outputs of the ANFIS models are recomposed to obtain the ultimate prediction results. The proposed method is validated by application to NASA lithium-ion battery experimental data. The results obtained show that the proposed method can obtain satisfactory prediction accuracy, wherein the negative impact of capacity regeneration on the prediction accuracy is reduced

A method for economic evaluation of predictive maintenance technologies by integrating system dynamics and evolutionary game modelling

International audiencePredictive maintenance technologies can be employed for failure prediction and system health management. Nevertheless, the additional cost involved in establishing the predictive maintenance system can be an obstacle to its widespread application. The decision on the predictive maintenance technology adoption can be made through the computation of the return on investment. To investigate the mechanisms of dynamic game between stakeholders involved in predictive maintenance, we establish the SD-EGT model from the perspective of systems engineering. This paper aims to propose an integrated method for the economic evaluation of predictive maintenance technologies by considering the incremental costs and benefits associated with its deployment. As an exemplary case, we take the Lithium-ion batteries whose failures have led to unexpected safety accidents. Firstly, we construct a quantitative relationship model between the failure modes and the predictive benefits of Lithium-ion battery systems to quantify the incremental benefits. Then, we establish a cost-benefit analysis (CBA) model by using system dynamics (SD) to make decisions about cost-effectiveness. Secondly, to optimize the cost investment strategy for the predictive maintenance technology, we develop an enterprise-government evolutionary game model, considering the information asymmetry between players. Eventually, we conduct a sensitivity analysis of the static subsidy strategy. The proposed methodology is serviceable to optimize the decision-making of predictive maintenance technology investment, which is a difficult yet very important task in industrial practice