Non-Stationary Process Monitoring for Change-Point Detection With Known Accuracy: Application to Wheels Coating Inspection

International audienceThis paper addresses the problem of monitoring online a non-stationary process to detect abrupt changes in the process mean value. Two main challenges are addressed: First, the monitored process is nonstationary; i.e., naturally changes over time and it is necessary to distinguish those “regular”process changes from abrupt changes resulting from potential failures. Second, this paper aims at being applied for industrial processes where the performance of the detection method must be accurately controlled. A novel sequential method, based on two fixed-length windows, is proposed to detect abrupt changes with guaranteed accuracy while dealing with non-stationary process. The first window is used for estimating the non-stationary process parameters, whereas the second window is used to execute the detection. A study on the performances of the proposed method provides analytical expressions of the test statistical properties. This allows to bound the false alarm probability for a given number of observations while maximizing the detection power as a function of a given detection delay. The proposed method is then applied for wheels coating monitoring using an imaging system. Numerical results on a large set of wheel images show the efficiency of the proposed approach and the sharpness of the theoretical study

Statistical decision methods in the presence of linear nuisance parameters and despite imaging system heteroscedastic noise: Application to wheel surface inspection

International audienceThis paper proposes a novel method for fully automatic anomaly detection on objects inspected using an imaging system. In order to address the inspection of a wide range of objects and to allow the detection of any anomaly, an original adaptive linear parametric model is proposed; The great flexibility of this adaptive model offers highest accuracy for a wide range of complex surfaces while preserving detection of small defects. In addition, because the proposed original model remains linear it allows the application of the hypothesis testing theory to design a test whose statistical performances are analytically known. Another important novelty of this paper is that it takes into account the specific heteroscedastic noise of imaging systems. Indeed, in such systems, the noise level depends on the pixels’ intensity which should be carefully taken into account for providing the proposed test with statistical properties. The proposed detection method is then applied for wheels surface inspection using an imaging system. Due to the nature of the wheels, the different elements are analyzed separately. Numerical results on a large set of real images show both the accuracy of the proposed adaptive model and the sharpness of the ensuing statistical test

Système de vision pour l'inspection et la surveillance de surface : application à l'inspection de roues

Visual inspection of finished products has always been one of the basic and most recognized applications of quality control in any industry. This inspection remains largely a manual process conducted by operators, and thus faces considerable limitations that make it unreliable. Therefore, it is necessary to automatize this inspection for better efficiency. The main goal of this thesis is to design an automatic visual inspection system for surface inspection and monitoring. The specific application of wheel inspection is considered to study the design and installation setup of the imaging system. Then, two inspection methods are developed: a defect detection method on the product's surface and a change-point detection method in the parameters of the non-stationary inspection process. Because in anindustrial context it is necessary to control the false alarm rate, the two proposed methods are cast into the framework of hypothesis testing theory. A parametric approach is proposed to model the non-anomalous part of the observations. The model parameters are estimated to design a statistical test whose performances are analytically known. Finally, the impact of illumination degradation on the defect detection performance is studied in order to predict the maintenance needs of the imaging system. Numerical results on a large set of real images highlight the relevance of the proposed approach.L'inspection visuelle des produits industriels a toujours été l'une des applications les plus reconnues du contrôle de qualité. Cette inspection reste en grande partie un processus manuel mené par des opérateurs et ceci rend l'opération peu fiable. Par conséquent, il est nécessaire d'automatiser cette inspection pour une meilleure efficacité. L'objectif principal de cette thèse est de concevoir un système d'inspection visuelle automatique pour l'inspection et la surveillance de la surface du produit. L'application spécifique de l'inspection de roues est considérée pour étudier la conception et l'installation du système d'imagerie. Ensuite, deux méthodes d'inspection sont développées : une méthode de détection des défauts à la surface du produit et une méthode de détection d'un changement brusque dans les paramètres du processus d'inspection non stationnaire. Parce que dans un contexte industriel, il est nécessaire de contrôler le taux de fausses alarmes, les deux méthodes proposées s'inscrivent dans le cadre de la théorie de la décision statistique. Un modèle paramétrique des observations est développé. Les paramètres du modèle sont estimés afin de concevoir un test statistique dont les performances sont analytiquement connues. Enfin, l'impact de la dégradation de l'éclairage sur la performance de détection des défauts est étudié afin de prédire les besoins de maintenance du systèmed'imagerie. Des résultats numériques sur un grand nombre d'images réelles mettent en évidence la pertinence de l'approche proposée

Système de vision automatique pour l'inspection et la surveillance de surface : application à l'inspection de roues

Visual inspection of finished products has always been one of the basic and most recognized applications of quality control in any industry. This inspection remains largely a manual process conducted by operators, and thus faces considerable limitations that make it unreliable. Therefore, it is necessary to automatize this inspection for better efficiency. The main goal of this thesis is to design an automatic visual inspection system for surface inspection and monitoring. The specific application of wheel inspection is considered to study the design and installation setup of the imaging system. Then, two inspection methods are developed: a defect detection method on product surface and a change-point detection method in the parameters of the non-stationary inspection process. Because in an industrial context it is necessary to control the false alarm rate, the two proposed methods are cast into the framework of hypothesis testing theory. A parametric approach is proposed to model the non-anomalous part of the observations. The model parameters are estimated to design a statistical test whose performances are analytically known. Finally, the impact of illumination degradation on the defect detection performance is studied in order to predict the maintenance needs of the imaging system. Numerical results on a large set of real images highlight the relevance of the proposed approach.L'inspection visuelle des produits industriels a toujours été l'une des applications les plus reconnues du contrôle de qualité. Cette inspection reste en grande partie un processus manuel mené par des opérateurs et ceci rend l'opération peu fiable.Par conséquent, il est nécessaire d'automatiser cette inspection pour une meilleure efficacité. L'objectif principal de cette thèse est de concevoir un système d'inspection visuelle automatique pour l'inspection et la surveillance de la surface du produit.L'application spécifique de l'inspection de roues est considérée pour étudier la conception et l'installation du système d'imagerie. Ensuite, deux méthodes d'inspection sont développées: une méthode de détection des défauts à la surface du produit et une méthode de détection d'un changement brusque dans les paramètres du processus d'inspection non stationnaire. Parce que dans un contexte industriel, il est nécessaire de contrôler le taux de fausses alarmes, les deux méthodes proposées s'inscrivent dans le cadre de la théorie de la décision statistique. Un modèle paramétrique des observations est développé.Les paramètres du modèle sont estimés afin de concevoir un test statistique dont les performances sont analytiquement connues. Enfin, l'impact de la dégradation de l'éclairage sur la performance de détection des défauts est étudié afin de prédire les besoins de maintenance du système d'imagerie. Des résultats numériques sur un grand nombre d'images réelles mettent en évidence la pertinence de l'approche proposée

Fully automatic detection of anomalies on wheels surface using an adaptive accurate model and hypothesis testing theory

International audienceThis paper studies the detection of anomalies, or defects, on wheels' surface. The wheel surface is inspected using an imaging system, placed over the conveyor belt. Due to the nature of the wheels, the different elements are analyzed separately. Because many different types of wheels can be manufactured, it is proposed to detect any anomaly using a general and original adaptive linear parametric model. The adaptivity of the proposed model allows us to describe accurately the inspected wheel surface. In addition, the use of a linear parametric model allows the application of hypothesis testing theory to design a test whose statistical performances are analytically known. Numerical results show the accuracy and the relevance of the proposed methodolog

Automatic vision system for wheel surface inspection and monitoring

International audienceIn order to stand out from the competition, the only alternative for automotive industries, including wheels manufacturing industries, is to grant lot of importance to their products quality. In recent years, most of the customer returns of defective products are due to appearance defects, which are associated with the wheel aesthetics. These defects are located on the outside of the wheel and are mainly related to the quality of the painting. In general, the defect detection process is a manual process conducted by operators, which is subjective and difficult due to the complicated wheels surface. This paper proposes to design a fully automatic computer vision system to inspect the whole surface of the wheel. It is proposed to use four cameras placed over the production line. A diffused lighting system is considered in order to illuminate homogeneously the whole surface of the wheel. Each wheel is designed with specific parameters that define its form and geometry. For defect detection, an original adaptive linear parametric model is proposed. This model is sufficiently general to describe any type of wheels. The adaptivity of the proposed model makes it sufficiently accurate to describe the inspected wheel surface while detecting small defects by using an optimal statistical hypothesis test. In addition, the computer vision system monitors in real time the wheel coating intensity. Using a parametric sequential method, this monitoring allows the detection of any abrupt changes, i.e. small decreasing amount of paint, that would reveal a sudden problem in the painting process

Détection de fissures par courants de Foucault en utilisant la divergence de Kullback-Leibler

National audienc