Optimisation de combinaisons de stratégies de maintenances par simulation stochastique

International audienceOptimisation de combinaisons de stratégies de maintenances par simulation stochastiqu

Optimisation de stratégies de maintenances par simulation stochastique AltaRica 3.0

International audienceLa maintenance des systèmes industriels de production est un des enjeux actuels majeurs. Dans ce cadre la gestion de leurs maintenances est un facteur important de compétitivité. Une bonne stratégie de maintenance permet en effet une disponibilité élevée du système, tout en minimisant les coûts liés aux interventions. Il est pour cela possible d'utiliser différents types de maintenances: les maintenances correctives qui réparent le système après les occurrences de défaillances, et les maintenances préventives qui interviennent sur le système avant les occurrences des défaillances. Cet article propose une méthode de couplage entre de la simulation stochastique de modèles à événements discrets AltaRica 3.0, et d’algorithmes d’optimisation, dans un objectif d’optimisation de la stratégie des maintenances d’un système. Le modèle AltaRica 3.0 représente l'ensemble des composants du système, avec les défaillances et les maintenances associées. L’idée est de dirigée la simulation du modèle suivant des paramètres obtenus par un algorithme d’optimisation, afin d’obtenir une, ou un ensemble de stratégies de maintenances optimales suivant des critères technico-économiques donnés (la disponibilité, les coûts de production ou d'exploitation, etc.)

3D objects descriptors method for fault detection in a multi sensors context

International audienceThe monitoring of an asset in an industrial context is a real challenge today, as data are more and more available, and computation power becomes cheaper with time. However, if we want to use data from different sensors to detect if there are anomalies of any kind, it is usually needed to individually consider a whole time series, or the values of several time series at a particular moment. In this article, we propose an adaptation of 3D object description methods to the context of the detection of unknown multi-sensors fault. This allows to detect an unknown problem to come on an asset monitored by several sensors. To our knowledge, this problem has not been completely solved yet, and opens new opportunities in class disequilibrium contexts. Final performances confirm the interest of the proposed approach adapted to a real time industrial context, and allow to open a new way of extracting features in the pretreatment of multi time series

Simulation based optimization for maintenance strategies using Altarica 3.0

The maintenance of industrial production systems is one of the major actual challenges, and the management of their maintenances is an important competitiveness factor. In fact a suitable maintenance strategy increases the system availability, and decreases costs due to interventions. Different kinds of maintenance policies can be used: corrective ones to repair the system after failures, preventive ones to maintain the system before failure. Based on a system case study, this work is a contribution to the field of simulation-based optimisation of maintenance strategies. The goal is to plan preventive maintenances according to two objectives: system availability and cost minimisation. Stochastic Discrete Event Simulation model was developed using Altarica 3.0 tool and connected to a Multi-objective optimisation algorithm (NSGAII). Both simulation and optimization modules interact with each other according to an iterative loop. The optimization combines statistics, coming from previous iteration, and the NSGAII algorithm provide to the simulation a new set of parameters for the model (the dates of maintenances), then the outputs of the simulation are performed in a new iteration. The experiments show that the NSGAII based approach achieved the best results although the computation time was relatively high and give more flexibility to the decision maker and give a trade-off between the two objectives

3D objects descriptors method for fault detection in a multi sensors context

International audienceThe monitoring of an asset in an industrial context is a real challenge today, as data are more and more available, and computation power becomes cheaper with time. However, if we want to use data from different sensors to detect if there are anomalies of any kind, it is usually needed to individually consider a whole time series, or the values of several time series at a particular moment. In this article, we propose an adaptation of 3D object description methods to the context of the detection of unknown multi-sensors fault. This allows to detect an unknown problem to come on an asset monitored by several sensors. To our knowledge, this problem has not been completely solved yet, and opens new opportunities in class disequilibrium contexts. Final performances confirm the interest of the proposed approach adapted to a real time industrial context, and allow to open a new way of extracting features in the pretreatment of multi time series

Optimisation de stratégies de maintenances par simulation stochastique AltaRica 3.0

International audienceLa maintenance des systèmes industriels de production est un des enjeux actuels majeurs. Dans ce cadre la gestion de leurs maintenances est un facteur important de compétitivité. Une bonne stratégie de maintenance permet en effet une disponibilité élevée du système, tout en minimisant les coûts liés aux interventions. Il est pour cela possible d'utiliser différents types de maintenances: les maintenances correctives qui réparent le système après les occurrences de défaillances, et les maintenances préventives qui interviennent sur le système avant les occurrences des défaillances. Cet article propose une méthode de couplage entre de la simulation stochastique de modèles à événements discrets AltaRica 3.0, et d’algorithmes d’optimisation, dans un objectif d’optimisation de la stratégie des maintenances d’un système. Le modèle AltaRica 3.0 représente l'ensemble des composants du système, avec les défaillances et les maintenances associées. L’idée est de dirigée la simulation du modèle suivant des paramètres obtenus par un algorithme d’optimisation, afin d’obtenir une, ou un ensemble de stratégies de maintenances optimales suivant des critères technico-économiques donnés (la disponibilité, les coûts de production ou d'exploitation, etc.)

Fault Detection in a Multi Sensors Context by 3D Object Descriptors Method

International audienceThe monitoring of an asset in an industrial context is a real challenge today, as data are more and more available, and computation power becomes cheaper with time. However, if we want to use data from different sensors to detect if there are anomalies of any kind, it is usually needed to individually consider a whole time series, or the values of several time series at a particular moment. In this article, we propose an adaptation of 3D objects descriptors to the detection of unknown faults in a multi-sensors context for features extraction. Then, classical outliers detection methods such as Local Outlier Factor1 and isolation forests 2 are used. This allows us to detect an unknown problem to come on an asset monitored by several sensors. To our knowledge, this problem has not been completely solved yet, and opens new opportunities in class disequilibrium contexts. Final performances confirm the interest of the proposed approach adapted to a real time industrial context, and allow to consider a new way for extracting features in the pretreatment of multi-time series

Fault Detection in a Multi Sensors Context by 3D Object Descriptors Method

International audienceThe monitoring of an asset in an industrial context is a real challenge today, as data are more and more available, and computation power becomes cheaper with time. However, if we want to use data from different sensors to detect if there are anomalies of any kind, it is usually needed to individually consider a whole time series, or the values of several time series at a particular moment. In this article, we propose an adaptation of 3D objects descriptors to the detection of unknown faults in a multi-sensors context for features extraction. Then, classical outliers detection methods such as Local Outlier Factor1 and isolation forests 2 are used. This allows us to detect an unknown problem to come on an asset monitored by several sensors. To our knowledge, this problem has not been completely solved yet, and opens new opportunities in class disequilibrium contexts. Final performances confirm the interest of the proposed approach adapted to a real time industrial context, and allow to consider a new way for extracting features in the pretreatment of multi-time series

Optimisation de combinaisons de stratégies de maintenances par simulation stochastique

International audienceOptimisation de combinaisons de stratégies de maintenances par simulation stochastiqu

Iterated Local Search Algorithm for the Constrained Two-Dimensional Non-Guillotine Cutting Problem

International audienceAn Iterated Local Search method for the constrained two-dimensional non-guillotine cutting problem is presented. This problem consists in cutting pieces from a large stock rectangle to maximize the total value of pieces cut. In this problem, we take into account restrictions on the number of pieces of each size required to be cut. It can be classified as 2D-SLOPP (two dimensional single large objects placement problem) and has many industrial applications like in wood and steel industries. The proposed Iterated Local Search algorithm in which we use a constructive heuristic and a local search move based on reducing pieces. The algorithm is tested on well known instances from the literature. Our computational results are very competitive compared to the best known solutions of literature and improve a part of them