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Gestion dynamique des connaissances de maintenance pour des environnements de production de haute technologie à fort mix produit

Anis Ben Said 1
1 G-SCOP_SIREP [2016-2019] - Système d’Information, conception RobustE des Produits [2016-2019]
G-SCOP - Laboratoire des sciences pour la conception, l'optimisation et la production
Abstract : The constant progress in electronic technology, the short commercial life of products, and the increasing diversity of customer demand are making the semiconductor industry a production environment constrained by the continuous change of product mix and technologies. In such environment, success depends on the ability to develop and industrialize new products in required competitive time while keeping a good level of cost, yield and cycle time criteria. These criteria can be ensured by high and sustainable availability of production capacity which needs appropriate maintenance policies in terms of diagnosis, supervision, planning and operating protocols. At the start of this study, the FMEA approach (analysis of failure modes, effects and criticality) was only mobilized to capitalize the expert’s knowledge for maintenance policies management. However, the evolving nature of the industrial context requires knowledge updating at appropriate frequencies in order to adapt the operational procedures to equipment and processes behavior changes.This thesis aims to show that the knowledge update can be organized by setting up an operational methodology combine both Bayesian networks and FMEA method. In this approach, existing knowledge and know-how skills are initially capitalized in terms of cause to effect links using the FMEA method in order to prioritize maintenance actions and prevent their consequences on the equipment, the product quality and personal safety. This knowledge and expertise are then used to develop unified operating procedures for expert’s knowledge and know-how sharing. The causal links stored in the FMEA are modeled in an operational Bayesian network (BN-O), in order to enable the assessment of maintenance actions effectiveness and, hence, the relevance of existing capitalized knowledge. In an uncertain and highly variable environment, the proper execution of procedures is measured using standards maintenance performance measurement indicators (MPM). Otherwise, the accuracy of existing knowledge can be assessed as a function of the O-BN model accuracy. Any drift of these criteria leads to learning a new unsupervised Bayesian network (U-BN) to discover new causal relations from historical data. The structural difference between O-BN (built using experts judgments) and U-BN (learned from data) highlights potential new knowledge that need to be analyzed and validated by experts to modify the existing FMEA and update associated maintenance procedures.The proposed methodology has been tested in a production workshop constrained by high product mix to demonstrate its ability to dynamically renew expert knowledge and improve the efficiency of maintenance actions. This experiment led to 30% decrease in failure occurrence due to inappropriate maintenance actions. This is certifying a better quality of knowledge modeled in the tools provided by this thesis.
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Submitted on : Tuesday, July 5, 2016 - 5:18:07 PM
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  • HAL Id : tel-01342332, version 1


Anis Ben Said. Gestion dynamique des connaissances de maintenance pour des environnements de production de haute technologie à fort mix produit. Autre. Université Grenoble Alpes, 2016. Français. ⟨NNT : 2016GREAI017⟩. ⟨tel-01342332⟩



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