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

Shapelet-based remaining useful life estimation.

International audienceIn the Prognostics and Health Management domain, estimating the remaining useful life (RUL) of critical machinery is a challenging task. Various research topics as data acquisition and processing, fusion, diagnostics, prognostivs and decision are involved in this domain. This paper presents an approach for estimating the Remaining Useful Life (RUL) of equipments based on shapelet extraction and characterization. This approach makes use in a first step of an history of run-to-failure data to extract discriminative rul-shapelets, i.e. shapelets that are correlated with the RUL of the considered equipment. A library of rul-shapelets is extracted from this step. Then, in an online step, these rul-shapelets are compared to different test units and the ones that match these units are used to estimate their RULs. This approach is hence different from classical similarity-based approaches that matches the test units with training ones. Here, discriminative patterns from the training set are first extracted and then matched to test units. The performance of our approach is assessed on a data set coming from a previous PHM Challenge. We show that this approach is efficient to estimate the RUL compared to other approaches

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

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

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

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

Case-based maintenance : Structuring and incrementing the Case.

International audienceTo avoid performance degradation and maintain the quality of results obtained by the case-based reasoning (CBR) systems, maintenance becomes necessary, especially for those systems designed to operate over long periods and which must handle large numbers of cases. CBR systems cannot be preserved without scanning the case base. For this reason, the latter must undergo maintenance operations.The techniques of case base’s dimension optimization is the analog of instance reduction size methodology (in the machine learning community). This study links these techniques by presenting case-based maintenance in the framework of instance based reduction, and provides: first an overview of CBM studies, second, a novel method of structuring and updating the case base and finally an application of industrial case is presented.The structuring combines a categorization algorithm with a measure of competence CM based on competence and performance criteria. Since the case base must progress over time through the addition of new cases, an auto-increment algorithm is installed in order to dynamically ensure the structuring and the quality of a case base. The proposed method was evaluated through a case base from an industrial plant. In addition, an experimental study of the competence and the performance was undertaken on reference benchmarks. This study showed that the proposed method gives better results than the best methods currently found in the literature

A methodology to conceive a case based system of industrial diagnosis.

International audienceThe objective of this paper is to address the diagnosis knowledge-oriented system in terms of artificial intelligence, particular by the Case-Based Reasoning (CBR) approach. Indeed, the use of CBR, which is an approach to problem solving and learning, in diagnosis goes back to a long time with the appearance of diagnostic support systems based on CBR. A diagnostic system by CBR implements an expertise-base composed of past experiences through which the origins of failure and the maintenance strategy are given according to a description of a specific situation of diagnostic. A study is made on the different diagnostic systems based on CBR. This study showed that there was no common methodology for building a CBR system. This design depends primarily on the case representation and knowledge models of the domain application. Consequently, this paper proposes a general design approach of a diagnostic system based on the CBR approach

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

PETRA: Process Evolution using a TRAce-based system on a maintenance platform

To meet increasing needs in the field of maintenance, we studied the dynamic aspect of process and services on a maintenance platform, a major challenge in process mining and knowledge engineering. Hence, we propose a dynamic experience feedback approach to exploit maintenance process behaviors in real execution of the maintenance platform. An active learning process exploiting event log is introduced by taking into account the dynamic aspect of knowledge using trace engineering. Our proposal makes explicit the underlying knowledge of platform users by means of a trace-based system called “PETRA”. The goal of this system is to extract new knowledge rules about transitions and activities in maintenance processes from previous platform executions as well as its user (i.e. maintenance operators) interactions. While following a Knowledge Traces Discovery process and handling the maintenance ontology IMAMO, “PETRA” is composed of three main subsystems: tracking, learning and knowledge capitalization. The capitalized rules are shared in the platform knowledge base in order to be reused in future process executions. The feasibility of this method is proven through concrete use cases involving four maintenance processes and their simulation