Abdessamad Kobi

Distribution of the different species of the Pseudallescheria boydii/Scedosporium apiospermum complex in French patients with cystic fibrosis.

As various new sibling species within the Pseudallescheria boydii/Scedosporium apiospermum complex have been described recently with differences in their susceptibility to antifungals, this study was conducted in order to determine their respective frequency in cystic fibrosis. Results indicated that P. boydii largely predominated (62%), followed by S. apiospermum (24%), Scedosporium aurantiacum (10%) and Pseudallescheria minutispora (4%). Scedosporium dehoogii was not recovered in this study. The multiple correspondence factor analysis highlighted geographical discrepancies within species distribution: P. boydii was rarely encountered in Northern France, while S. apiospermum was less represented in the west of the country. Additionally, we demonstrated that all species encountered in the cystic fibrosis context were capable to chronically colonize the respiratory tract of patients. Molecular typing of a large set of environmental and clinical isolates should be conducted to delineate the epidemiology of each sibling species in the complex.

Human-impacted areas of France are environmental reservoirs of the Pseudallescheria boydii/Scedosporium apiospermum species complex.

Species of the Pseudallescheria boydii/Scedosporium apiospermum complex (PSC) are emerging fungal pathogens able to chronically colonize the airways of patients with cystic fibrosis (CF). As P. boydii was found more frequently colonizing the lungs of CF patients in France than in other European countries in a previous report, the present study was conducted in order to clarify distribution of PSC species in France and to characterize their natural habitat. The highest densities of PSC isolates were found in human-impacted areas, i.e. agricultural areas, fluids obtained from wastewater treatment plants, playgrounds and industrial areas. PSC was not detected from soil samples collected in forests. Most PSC culture-positive soil samples exhibited a pH range of 6-8. Scedosporium dehoogii, the most abundant species, was detected in all human-impacted area types except vineyards, whereas Scedosporium aurantiacum was mostly found in agricultural areas. Pseudallescheria boydii and S. apiospermum were predominantly isolated from seashores and playgrounds respectively. Pseudallescheria minutispora was found only once from a playground. This study highlights potential sources of contamination of the patients, especially in the CF context.

A Relationship Between Six Sigma and ISO 9000:2000

We propose a strategy for the implementation of the Six Sigma method as an improvement solution for the ISO 9000:2000 Quality Standard. Our approach is focused on integrating the DMAIC cycle of the Six Sigma method with the PDCA process approach, highly recommended by the standard ISO 9000:2000. The Six Sigma steps applied to each part of the PDCA cycle are presented in detail, along with some tools and training examples. Based on this analysis, the authors conclude that applying Six Sigma philosophy to the quality standard implementation process is the best way to achieve the optimal results in quality progress and therefore in customer satisfaction.

Fault diagnosis of industrial systems by conditional Gaussian network including a distance rejection criterion

The purpose of this article is to present a method for industrial process diagnosis with Bayesian network, and more particularly with conditional Gaussian network (CGN). The interest of the proposed method is to combine a discriminant analysis and a distance rejection in a CGN in order to detect new types of fault. The performances of this method are evaluated on the data of a benchmark example: the Tennessee Eastman Process. Three kinds of fault are taken into account on this complex process. The challenging objective is to obtain the minimal recognition error rate for these three faults and to obtain sufficient results in rejection of new types of fault.

Lifetime multiple response optimization of metal extrusion die

The aim of this paper is to optimize the lifetime of dies for the multiple response in metal extrusion process. The main objective of the multiple response optimization is to improve the quality of a product or process by minimizing the effects of variation without eliminating the causes. Numerical simulations with ABAQUS software obtain the results of the tests.

Application of design of experiment technique for metal blanking processes optimization

The blanking of metal parts is subjected to a variety of process parameters. In this paper, an experimental investigation of the blanking process was carried out using tools with four different wear states and four different clearances. The aim was to study the effects of the interaction between the clearance, the wear state of the tool and the sheet metal thickness on the evolution of the blanking force and the geometry of the sheared profile. Designed experiments are an efficient and cost-effective way to model and analyze the relationships that describe process variations. The results of the proposed experimental investigation show that there is no universal optimal clearance value. Whether clearance should be set at 5% or 10% ultimately depends on the priorities of the practitioners. In the present investigation, mathematical relationship describing the fracture zone depth, the fracture angle and the blanking force are proposed.

Distance rejection in a bayesian network for fault diagnosis of industrial systems

The purpose of this article is to present a method for industrial process diagnosis with Bayesian network. The interest of the proposed method is to combine a discriminant analysis and a distance rejection in a bayesian network in order to detect new types of fault. The performances of this method are evaluated on the data of a benchmark example: the Tennessee Eastman Process. Three kinds of fault are taken into account on this complex process. The challenging objective is to obtain the minimal recognition error rate for these three faults and to obtain sufficient results in rejection of new types of fault.

Fault Diagnosis with Bayesian Networks: Application to the Tennessee Eastman Process

The purpose of this article is to present and evaluate the performance of a new procedure for industrial process diagnosis. This method is based on the use of a Bayesian network as a classifier. But, as the classification performances are not very efficient in the space described by all variables of the process, an identification of important variables is made. This feature selection is made by computing the mutual information between each process variable and the class variable. The performances of this method are evaluated on the data of a benchmark problem: the Tennessee Eastman process. Three kinds of faults are taken into account on this complex process. The objective is to obtain the minimal recognition error rate for these 3 faults. Results are given and compared with results of other authors on the same data.

A Bayesian network dealing with measurements and residuals for system monitoring

The purpose of this paper is to present an original method for system monitoring with Bayesian networks. Our proposal is to associate a data-driven method to another model-based under a common tool. The two methods are first modeled under a Bayesian network (conditional Gaussian network), and then combined to evaluate the system state. In the proposed framework the residuals and measures coexist under a probabilistic framework. This approach is tested on a simulation of a water heater process under some various circumstances and shows better results than the two methods used alone.

Fault detection with Conditional Gaussian Network

The main interest of this paper is to illustrate a new representation of the Principal Component Analysis (PCA) for fault detection under a Conditional Gaussian Network (CGN), a special case of Bayesian networks. PCA and its associated quadratic statistics such as T2 and SPE are integrated under a sole CGN. The proposed framework projects a new observation into an orthogonal space and gives probabilities on the state of the system. It could do so even when some data in the sample test are missing. This paper also gives the probabilities thresholds to use in order to match quadratic statistics decisions. The proposed network is validated and compared to the standard PCA scheme for fault detection on the Tennessee Eastman Process and the Hot Forming Process.