Worst-case global optimization of black-box functions through Kriging and relaxation

International audienceA new algorithm is proposed to deal with the worst-case optimization of black-box functions evaluated through costly computer simulations. The input variables of these computer experiments are assumed to be of two types. Control variables must be tuned while environmental variables have an undesirable effect, to which the design of the control variables should be robust. The algorithm to be proposed searches for a minimax solution, i.e., values of the control variables that minimize the maximum of the objective function with respect to the environmental variables. The problem is particularly difficult when the control and environmental variables live in continuous spaces. Combining a relaxation procedure with Kriging-based optimization makes it possible to deal with the continuity of the variables and the fact that no analytical expression of the objective function is available in most real-case problems. Numerical experiments are conducted to assess the accuracy and efficiency of the algorithm, both on analytical test functions with known results and on an engineering application

Cooperative fault detection and isolation in a surveillance sensor network: a case study

International audienceThis work focuses on Fault Detection and Isolation (FDI) among sensors of a surveillance network. A review of the main characteristics of faults in sensor networks and the associated diagnosis techniques is first proposed. An extensive study has then been performed on the case study of the persistent monitoring of an area by a sensor network which provides binary measurements of the occurrence of events to be detected (intrusions). The performance of a reference FDI method with and without simultaneous intrusions has been quantified through Monte Carlo simulations. The combination of static and mobile sensors has also been considered and shows a significant performance improvement for the detection of faults and intrusions in this context

Learning viewpoint planning in active recognition on a small sampling budget: a Kriging approach

International audienceThis paper focuses on viewpoint planning for 3D active object recognition. The objective is to design a planning policy into a Q-learning framework with a limited number of samples. Most existing stochastic techniques are therefore inapplicable. We propose to use Kriging and Bayesian Optimization coupled with Q-learning to obtain a computationally-efficient viewpoint-planning design, under a restrictive sampling budget. Experimental results on a representative database, including a comparison with classical approaches, show promising results for this strategy

A new strategy for worst-case design from costly numerical simulations

International audienceWorst-case design is important whenever robustness to adverse environmental conditions should be ensured regardless of their probability. It leads to minimax optimization, which is most often considered assuming that a closed-form expression for the performance index is available. In this paper, we consider the important situation where this is not the case and where evaluation of the performance index is via costly numerical simulations. In this context, strategies to limit the number of these evaluations are of paramount importance. This paper describes one such strategy, which further improves the performance of an algorithm recently presented that combines the use of a relaxation procedure for minimax search and Kriging-based efficient global optimization. Test cases from the literature demonstrate the interest of the approach

Nonlinear FDI based on state derivatives, as provided by inertial measurement units

International audienceVarious strategies based on dierential geometry or system inversion have been proposed to deal with fault detection and isolation (FDI) for nonlinear systems. Many of them require the computation of successive derivatives of inputs and outputs, which might be unrealistic in practical applications where measurements suer noise and disturbances. In this paper, we take advantage of the fact that, in domains such as aerospace or robotics, sensors allow the measurement of rst-order derivatives of state variables. This information, along with the redundancy provided by the control module can be used to generate residuals. Such a procedure is proposed and applied to a generic 2D aeronautical case study

Réglage automatique et comparaison de méthodes de diagnostic par krigeage

National audienceLa plupart des méthodes de diagnostic dépendent de paramètres internes, ou hyperparamètres. Le réglage automatique de ces hyperparamètres est un problème important qui conditionne fortement les performances de ces approches. Il est ici traité comme un problème d'optimisation, en supposant qu'il est possible de simuler un cas test numériquement afin d'évaluer les performances des différentes méthodes. La méthodologie proposée substitue à la simulation complexe initiale un modèle de krigeage, associé à une procédure itérative d'optimisation. Ceci permet d'aborder ce problème à un très faible coût de calcul. Deux applications de ce réglage à des méthodes de diagnostic sont présentées. La première concerne la détection de changement de moyenne dans un signal. La seconde a pour objet un schéma complet de détection de défaut, construit sur l'utilisation conjointe d'un générateur de résidus et d'un test statistique. Les performances de ces méthodes de diagnostic peuvent être ainsi comparées objectivement

Robust automatic tuning of diagnosis methods via an efficient use of costly simulations

International audienceThe robust tuning methodology developed in this paper aims at adjusting automatically the hyperparameters of fault-diagnosis procedures for complex case studies. The strategy should make an efficient use of computer simulations of these case studies, which will usually be computationally expensive. To this end, Kriging-based optimization is called upon. Robustness to environmental disturbances is achieved by continuous minimax optimization, and handled through an iterative relaxation procedure. This strategy is applied to the automatic tuning of a model-based fault-diagnosis scheme for a realistic aerospace application

Global extremum seeking by Kriging with a multi-agent system

Preprint 17th IFAC Symposium on System Identification, SYSID 2015This paper presents a method for finding the global maximum of a spatially varying field using a multi-agent system. A surrogate model of the field is determined via Kriging (Gaussian process regression) from a set of sampling measurements collected by the agents. A criterion exploiting Kriging statistical properties is introduced for selecting new sampling points that each vehicle must rally. These new points are obtained as a compromise between improvement of the estimate of the global maximum and traveling distance. A cooperative control law is proposed to move the agents to the desired sampling positions while avoiding collisions. Simulation results show the interest of the method and how it compares with a state-of-art solution

Model-based fault diagnosis for aerospace systems: a survey

http://pig.sagepub.com/content/early/2012/01/06/0954410011421717International audienceThis survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic nonlinear dynamical modeling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided

Cooperative estimation and fleet reconfiguration for multi-agent systems

International audienceThis paper considers a multi-agent system which aim is to determine the maximum of some field. For that purpose, noisy measurements are collected by each agent and exchanged between neighboring agents. The maximization task, performed by gradient climbing, has to be robust to the presence of agents equipped with sensors providing outliers. For that purpose, an outlier detection scheme is used and the optimal configuration for agents with different sensor noise characteristics is evaluated. This gives insights to derive a practical distributed control law to achieve robust maximization. The stability of the system with this control law is analyzed. The resulting performance is illustrated on an example