Case elaboration methodology proposed for diagnostic and repair help system based on CBR.

International audienceAlthough the elaboration of the case representation is the key problem of the case-based reasoning system conception there is no proved methodology targeted to this task for now. This paper deals with this lack in the maintenance domain precisely in the equipments diagnostic and repair help. A methodology of the case representation elaboration is proposed based on knowledge management techniques and existing engineering analytical tools used in the industry. Different ontological models are proposed to take into account similarity and adaptability aspects of the case representation and to optimize the case base size

Extraction de règles d'ordonnancement : Aide au paramétrage d'un progiciel d'ordonnancement.

International audienceConditions complexity of management workshop of production manufacturing increases and requires heuristic algorithm adapted to the context. The principal difficulty then lies in the choice of heuristic algorithm to apply. We propose a help method to schedule, and more specifically a parameter setting up's help of an industrial scheduling software, being based on machine learning system able to extract knowledge from data. An inductive learning based on examples system is developed and replaced in a process of ECD (Extraction of Knowledge starting from Data). The first step pf this process is specific to our problem and uses in this case the capacities of simulation of a market software of scheduling

A new contextual based feature selection.

International audienceThe pre processing phase is essential in Knowledge Data Discovery process. We study too particularly the data filtering in supervised context, and more precisely the feature selection. Our objective is to permit a better use of the data set. Most of filtering use myopic measures, and give bad results in the case of the features correlated part by part. Consequently in the first time, we build two new contextual criteria. In the second par, we introduce those criteria in an algorithm similar to the greedy algorithm. The algorithm is tested on a set of benchmarks and the results were compared with five reference algorithms : Relief, CFS, Wrapper (C4.5), consistancySubsetEval and GainRatio. Our experiments have shown its ability to detect the semi-correlated features. We conduct extensive experiments by using our algorithm like pre processing data for decision tree, nearest neighbours and Naïve Bays classifiers

Evolving class for SVM's incremental learning.

International audienceThe good generalization performance of support vector machines (SVM) has made them a popular tool in artificial intelligence community. In this paper, we prove that SVM multi class algorithms are not equivalent for all classification problems we present a new approach for incremental learning using SVM that create a rejection class which would be interesting for fault diagnosis where fault classes usually evolve with time : It is when some new samples may be rejected by all the current classes. Hence, these samples may correspond to a new fault (a new class) which may appear after the first training step

Classification des différentes architectures en maintenance.

L'objectif de ce papier est de lister et caractériser différents systèmes informatiques existants dans le domaine de la maintenance industrielle afin de proposer une classification des différentes architectures de ces systèmes. Deux critères de cette classification s'imposent : l'évolution de l'information utilisée et la relation entre les systèmes intégrés dans les architectures. Quatre architectures génériques sont identifiées, à savoir maintenance, télémaintenance, e-maintenance et s-maintenance. Le type d'architecture de maintenance sémantique : la s-maintenance est proposée prenant appui sur des ontologies communes aux différents systèmes et sur la technologie émergente du Web sémantique. Ce nouveau concept représente une architecture adaptée aux besoins d'intégrer les différents systèmes d'aide aux opérateurs et aux experts de maintenance et ouvre également la possibilité d'utiliser les techniques de gestion des connaissances dans ces systèmes

Towards a maintenance semantic architecture.

International audienceTechnological and software progress with the evolution of processes within company have highlighted the need to evolve systems of maintenance process from autonomous systems to cooperative and sharing information system based on software platform. However, this need gives rise to various maintenance platforms. The first part of this study investigates the different types of existing industrial platforms and characterizes them compared to two criteria namely : information exchange and relationship intensity. This allowed identifying the e-maintenance architecture as the current most efficient architecture. despite its effectiveness, this latter can only guarantee technical interoperability between various components. Therefore, the second part of this study proposes a semantic-knowledge based architecture, thereby ensuring a higher level of semantic interoperability. To this end, specific maintenance ontology has been developed

A formal ontology for industrial maintenance

International audienceThe rapid advancement of information and communication technologies has resulted in a variety of maintenance support systems and tools covering all sub-domains of maintenance. Most of these systems are based on different models that are sometimes redundant or incoherent and always heterogeneous. This problem has lead to the development of maintenance platforms integrating all of these support systems. The main problem confronted by these integration platforms is to provide semantic interoperability between different applications within the same environment. In this aim, we have developed an ontology for the field of industrial maintenance, adopting the METHONTOLOGY approach to manage the life cycle development of this ontology, that we have called IMAMO (Industrial MAintenance Management Ontology). This ontology can be used not only to ensure semantic interoperability but also to generate new knowledge that supports decision making in the maintenance process. This paper provides and discusses some tests so as to evaluate the ontology and to show how it can ensure semantic interoperability and generate new knowledge within the platform

Case Base Maintenance Approach.

International audienceCase base Maintenance is an active Case Based Reasoning research area. The main stream focuses on the method for reducing the size of the case-base while maintaining case-base competence. This paper gives an overview of these works, and proposes a case deletion strategy based on competence criteria using a novel approach. The proposed method, even if inspired from existing literature, combines an algorithm with a Competence Metric (CM). A series of tests are conducted using two standards data-sets as well as a locally constructed one, on which, three Case Base Maintenance approaches were tested. This experimental study shows how this technique compares favourably to more traditional strategies across two standard data-sets

A contextual semantic mediator for a distributed cooperative maintenance platform.

International audiencePlatforms expand maintenance systems from centralized systems into e-maintenance platforms integrating various cooperative distributed systems and maintenance applications. This phenomenon allowed an evolution in services offered to maintenance actors by integrating more intelligent applications, providing decision support and facilitating the access to needed data. To manage this evolution, e-maintenance platforms must respond to a great challenge which is ensuring an interoperable communication between its integrated systems. By combining different techniques used in previous works, we propose in this work a semantic mediator system ensuring a high level of interoperability between systems in the maintenance platform