Zohra Cherfi

A Comparative Study of CUSUM and EWMA Charts: Detection of Incipient Drifts in a Multivariate Framework

This contribution to the field of statistical process control concerns a multivariate control chart in the case of incipient process drifts. The design of this chart is based on the assumption that an a priori knowledge of the process operation states is available. The detection is based on a multimodal T 2 hoteling statistic and a dynamic model of the drift. The generated diagnosis makes possible the anticipation of the direction of the drift (toward a known or unknown operation state). The performances of this chart are compared with the classical T 2 chart using a set of simulated data.

Statistical modeling and optimization for diesel engine calibration

The European standards concerning the pollutants emissions of automotive engine become more and more severe. Modern automotive engines are equipped with an increasing number of new technologies and controlling elements. The consequence of this evolution, is the increase of the number of the controllable parameters, the difficulty to understand the engine behavior, and to find the parameters settings that offer the best compromise across the entire engine map, especially between fuel consumption and emissions constraints. This paper deals with problem of engine calibration, using the minimum of experiments. The approach proposed consists in building a global emulator based on Kriging model, which was adapted to take into account a number of control parameters greater than 3, while existing software are limited to two control parameters. This model is used to predict an engine response, and is coupled with a genetic algorithm, in order to give a best setting of parameters, optimizing the fuel consumption within constraints on the emission of NO∞ (nitrogen oxide). The main advantage of this approach is, its capacity to take into account a considerable number of controllable parameters in the optimization process, without lost in accuracy of model prediction.

Case Study: Color Control in the Automotive Industry

Numerous manufacturers are concerned with ensuring the homogeneity in the colors of different parts that compose their products. This quality objective of primary importance is especially challenging when the parts are sourced by different suppliers. The technical difficulties are accentuated when metallic or pearly paints are used since the presence of metallic chips in the paint causes a variation in the color according to the angle of observation. Also, the development of metametric colors increases these difficulties even more, as these colors are sensitive to different illumination sources. The present study relates to the production of colored car bumpers. By using a spectrocolorimeter and color samples, the process is improved to better respect the customer specifications. To achieve this, critical colors and significant parameters affecting colors are identified, design of experiments is used to optimize the process settings, and a correlation study allows further process improvement. The results achieved are impressive: the quality index used has been improved by 67% in only 6 months. This case study therefore illustrates how simple quality tools can be used in a rigorous search for process improvement toward total color mastering with zero defect objective.

Optimal initial state for fast parameter estimation in nonlinear dynamical systems.

BACKGROUND AND OBJECTIVEnThis paper deals with the improvement of parameter estimation in terms of precision and computational time for dynamical models in a bounded error context.nnnMETHODSnTo improve parameter estimation, an optimal initial state design is proposed combined with a contractor. This contractor is based on a volumetric criterion and an original condition initializing this contractor is given. Based on a sensitivity analysis, our optimal initial state design methodology consists in searching the minimum value of a proposed criterion for the interested parameters. In our framework, the uncertainty (on measurement noise and parameters) is supposed unknown but belongs to known bounded intervals. Thus guaranteed state and sensitivity estimation have been considered. An elementary effect analysis on the number of sampling times is also implemented to achieve the fast and guaranteed parameter estimation.nnnRESULTSnThe whole procedure is applied to a pharmacokinetics model and simulation results are given.nnnCONCLUSIONSnThe good improvement of parameter estimation in terms of computational time and precision for the case study highlights the potential of the proposed methodology.

Guaranteed state and parameter estimation for nonlinear dynamical aerospace models

This paper deals with parameter and state estimation in a bounded-error context for uncertain dynamical aerospace models when the input is considered optimized or not. In a bounded-error context, perturbations are assumed bounded but otherwise unknown. The parameters to be estimated are also considered bounded. The tools of the presented work are based on a guaranteed numerical set integration solver of ordinary differential equations combined with adapted set inversion computation. The main contribution of this work consists in developing procedures for parameter estimation whose performance is highly related with the input of system. In this paper, a comparison with a classical non-optimized input is proposed.

A new Bayesian technique for readjusting LOLIMOT models: example with diesel engine emissions

Constraints on diesel engine emissions have increased dramatically over the past ten years. In this situation, design of experiments (DoE) are generally used to model the engines exhaust emissions (EE) behaviour. The main drawback of parametric modelling is that, if the system evolves (e.g. new product development), then the model is no longer valid. Our proposition, focused on change management, is based on the Bayesian theory and presents two new algorithms. The aim of this paper is to outline a method for readjusting LOLIMOT models resulting from the DoE with as little data as possible, in order to optimise the EE of new engines. Two algorithms are presented: one use new data to readjust the model and the other, use both new data and expert judgement. We prove that Bayesian theory could be used to reduce the number of required test points and so, the cost of new product development.

Entrée optimale pour l’estimation des systèmes dynamiques non linéaires à erreurs bornées. Application en aéronautique

Le travail propose dans cet article concerne le developpement d’une methodologie d’obtention d’entree optimale pour l’estimation de parametres d’un modele dynamique non lineaire dans un contexte a erreurs bornees. Dans ce contexte, les bruits de mesure et parametres a estimer sont supposes varier dans des intervalles bornes, de bornes connues ; ne sont alors considerees dans les calculs que les bornes des intervalles. L’estimation des parametres du modele s’effectue en combinant les outils de l’integration numerique garantie et l’inversion ensembliste. L’obtention de l’entree optimale permettant une estimation plus precise des para- metres s’effectue en utilisant une analyse de sensibilites, ce qui est la contribution principale de notre travail. Un critere d’optimisation d’entree est developpe. La procedure d’obtention d’entree optimale presentee dans cet article est appliquee a un modele issu de l’aeronautique. Les resultats d’estimation de parametres obtenus en utilisant l’entree optimale sont compares a ceux obtenus avec une entree non optimale. Les resultats numeriques mettent en exergue le potentiel de cette approche.

Two stage approaches for modeling pollutant emission of diesel engine based on Kriging model

The automotive industry faces the competing goals of producing better performing vehicles and keeping development time with low costs. It is crucial for the manufacturers to be able to produce fuel-economic vehicles, which respect pollutant emissions standards, and which meet the customers expectations. Accordingly, the complexity of the engines responses we have to optimize and the number of the parameters to control during the design stage, have increased rapidly, in the later years. In order to deliver vehicles, which respond to these requirements, in a reasonable time scale. Companies use design of experiments (DOE) (Schimmerling et al., 1998) in one side, and modelling, in the other side. DOE is a power tool, but the cost of the experiments and their duration, particularly in the field of pollutant emissions, can be a limit to their use in automotive industry.

The Desirability Function in a Multiresponse Optimisation Framework: A Case Study

In this paper we present a review of the different approaches in a multi response optimisation design framework. Different criteria are reviewed and discussed. A comparative study is presented using different multi-responses optimization techniques. This study concerns the design of an insulation product

The T/sup 2/ control chart in a multimodal framework: detection of incipient trends

The aim of this paper is to propose a new statistic for monitoring multivariate trend processes. We focus on the possible choices of more sensitive statistics than the classical Hotelling T2 statistic in the case of processes where incipient trends are considered. A comparative study based on simulations is presented. The performances of detection are compared to the classical Hotelling control chart in terms of average run length ARL.