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Multi-objective system reliability optimization has attracted the attention of several researchers, due to its importance in industry. In practice, the optimization regards multiple objectives, for example, maximize the reliability, minimize the cost, weight, and volume. In this article, an adaptive particle swarm optimization is presented for multi-objective system reliability optimization. The approach uses a Lévy flight for some particles of the swarm, for avoiding local optima and insuring diversity in the exploration of the search space. The multi-objective problem is converted to a single-objective problem by resorting to the weighted-sum method and a penalty function is implemented to handle the constraints. Nine numerical case studies are presented as benchmark problems for comparison; the results show that the proposed approach has superior performance than a standard particle swarm optimization.

International audience; The migration of modern Industrial Control Systems (ICS) towards information and communication technologies exposes them to cyber-attacks that can alter the way they function, thereby causing adverse consequences on the system and its environment. It has consequently become crucial to consider security risks in traditional safety risk analyses for industrial systems controlled by modern ICS. We propose in this paper a new framework for safety and security joint risk analysis for industrial control systems. S-cube (for SCADA Safety and Security joint modeling) is a new model-based approach that enables, thanks to a knowledge base, formal modeling of the physical and functional architecture of cyber physical systems and automatic generation of a qualitative and quantitative analysis encompassing safety risks (accidental) and security risks (malicious). We first give the principle and rationale of S-cube then we illustrate its inputs and outputs on a case study.; La migration des systèmes de contrôle industriels (SCI) modernes vers les technologies de l'information et de la communication les expose à des cyberattaques susceptibles d'altérer leur fonctionnement, entraînant ainsi des conséquences néfastes pour le système et son environnement. Par conséquent, il est devenu crucial de prendre en compte les risques de sécurité dans les analyses de sûreté de fonctionnement traditionnelles pour les systèmes industriels contrôlés par un système ICS moderne. Nous proposons dans ce document un nouveau cadre d'analyse de risque commun pour la sûreté et la sécurité des systèmes de contrôle industriels. S-cube (pour la modélisation conjointe SCADA Safety and Security) est une nouvelle approche "model based" qui permet, grâce à une base de connaissances, une modélisation formelle de l'architecture physique et fonctionnelle de systèmes cyber physiques et la génération automatique d'une analyse qualitative et quantitative englobant les risques de sûreté (accidents) et risques de sécurité (malveillance). Nous donnons d’abord le principe et la raison d’être de S-cube, puis nous illustrons ses entrées et ses sorties sur une étude de cas.

Big data from very large fleets of assets challenge the asset management, as the number of maintenance strategies to optimize and administrate may become very large. To address this issue, we exploit a clustering approach that identifies a small number of sets of assets with similar reliability behaviors. This enables addressing the maintenance strategy optimization issue once for all the assets belonging to the same cluster and, thus, introduces a strong simplification in the asset management. However, the clustering approach may lead to additional maintenance costs, due to the loss of refinement in the cluster reliability model. For this, we propose a cost model to support asset managers in trading off the simplification brought by the cluster-based approach against the related extra costs. The proposed approach is applied to a real case study concerning a set of more than 30,000 switch point machines.

International audience; In this work, we consider the problem of predicting the remaining useful life of a piece of equipment, based on data collected from a heterogeneous fleet working under different operating conditions. When the equipment experiences variable operating conditions, individual data-driven prognostic models are not able to accurately predict the remaining useful life during the entire equipment life. The objective of this work is to develop an ensemble approach of different prognostic models for aggregating their remaining useful life predictions in an adaptive way, for good performance throughout the degradation progression. Two data-driven prognostic models are considered, a homogeneous discrete-time finite-state semi-Markov model and a fuzzy similarity–based model. The ensemble approach is based on a locally weighted strategy that aggregates the outcomes of the two prognostic models of the ensemble by assigning to each model a weight and a bias related to its local performance, that is, the accuracy in predicting the remaining useful life of patterns of a validation set similar to the one under study. The proposed approach is applied to a case study regarding a heterogeneous fleet of aluminum electrolytic capacitors used in electric vehicle powertrains. The results have shown that the proposed ensemble approach is able to provide more accurate remaining useful life predictions throughout the entire life of the equipment compared to an alternative ensemble approach and to each individual homogeneous discrete-time finite-state semi-Markov model and fuzzy similarity–based models.