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dc.contributor.authorDebbah, Younes
dc.contributor.authorCherfia, Abdelhakim
dc.date.accessioned2018-06-13T14:32:39Z
dc.date.available2018-06-13T14:32:39Z
dc.date.issued2018-05-28
dc.identifier.urihttp://hdl.handle.net/123456789/136353
dc.description.abstractMaintenance is becoming increasingly important in companies and tends to evolve for reactivity and cost needs. A particular evolution concerns the way to apprehend the phenomena of failure: little by little the industrialists tend, not only to anticipate them by the recourse to preventive actions, but in addition to do it in the most just possible way with a goal reducing costs and risks. This evolution has given a growing share to the prognosis process. The activity of fault prognosis is today considered as a key process in industrial maintenance strategies. However, in practice, prognostic tools are still rare. Today's stabilized approaches rely on a history of significant incidents to be representative of potentially predictable events The purpose of this thesis is to propose a tool to predict the degradation of equipment without prior knowledge of its behavior, and to generate prognostic indicators to optimize maintenance strategies. Various techniques, of vibratory signal processing, have been explored and tested, on a test bench designed and realized as part of the research axes of this work. Two techniques of artificial intelligence have been exploited in the diagnosis and prognosis of defects in rotating machines, where indicator selection techniques have been explored. The combination of vibration signal processing techniques and artificial intelligence by neural networks has made it possible to provide an efficient prognostic tool and to quantify the relevance of the sources of information used and proposed.fr_FR
dc.language.isofrfr_FR
dc.publisherجامعة الإخوة منتوري قسنطينةfr_FR
dc.subjectPronosticfr_FR
dc.subjectdiagnosticfr_FR
dc.subjectprédictionfr_FR
dc.subjectintelligence artificiellefr_FR
dc.subjectanalyse vibratoirefr_FR
dc.subjectsystèmes expertsfr_FR
dc.subjectréseaux de neuronesfr_FR
dc.subjectPrognosisfr_FR
dc.subjectdiagnosisfr_FR
dc.subjectpredictionfr_FR
dc.subjectartificial intelligencefr_FR
dc.subjectvibration analysisfr_FR
dc.subjectexpert systemsfr_FR
dc.subjectneural networksfr_FR
dc.subjectالذكاء الاصطناعيfr_FR
dc.subjectتحليل الاهتزازfr_FR
dc.subjectالنظم الخبيرةfr_FR
dc.subjectالشبكات العصبيةfr_FR
dc.subjectالتنبؤfr_FR
dc.subjectالتشخيصfr_FR
dc.titleDéveloppement d’un outil de pronostic pour la maintenance des systèmes mécaniques.fr_FR
dc.typeThesisfr_FR


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