%0 Conference Proceedings %T Online fuzzy temporal operators for complex system monitoring %+ Intelligence Artificielle et Apprentissage Automatique (LI3A) %+ Laboratoire Sciences des Données et de la Décision (LS2D) %A Poli, Jean-Philippe %A Boudet, Laurence %A Espinosa, Bruno %A Cornez, Laurence %Z Conference of 14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2017 ; Conference Date: 10 July 2017 Through 14 July 2017; Conference Code:194239 %< avec comité de lecture %( Lecture Notes in Computer Science book series (LNCS, volume 10369) %B ISIPTA '17 and ECSQARU 2017 - 14th European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty %C Lugano, Switzerland %Y Alessandro Antonucci %Y Laurence Cholvy %Y Odile Papini %3 Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017. Lecture Notes in Computer Science %V 10369 LNAI %P 375-384 %8 2017-07-10 %D 2017 %R 10.1007/978-3-319-61581-3_34 %K System monitoring %K Natural languages %K Mathematical definitions %K High-level information %K Fuzzy expert systems %K Event streams %K Online systems %K Large scale systems %K Expert systems %K Temporal operators %K Temporal relation %K Mathematical operators %K Computer hardware description languages %K online learning %K machine learning %K artificial intelligence %K fuzzy logic %Z Computer Science [cs]/Artificial Intelligence [cs.AI] %Z Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an] %Z Computer Science [cs]/Information Retrieval [cs.IR]Conference papers %X Online fuzzy expert systems can be used to process data and event streams, providing a powerful way to handle their uncertainty and their inaccuracy. Moreover, human experts can decide how to process the streams with rules close to natural language. However, to extract high level information from these streams, they need at least to describe the temporal relations between the data or the events. In this paper, we propose temporal operators which relies on the mathematical definition of some base operators in order to characterize trends and drifts in complex systems. Formalizing temporal relations allows experts to simply describe the behaviors of a system which lead to a break down or an ineffective exploitation. We finally show an experiment of those operators on wind turbines monitoring. %G English %2 https://cea.hal.science/cea-01809219/document %2 https://cea.hal.science/cea-01809219/file/article_JeanPhilippePoli_ECSQARU2017.pdf %L cea-01809219 %U https://cea.hal.science/cea-01809219 %~ CEA %~ DRT %~ CEA-UPSAY %~ UNIV-PARIS-SACLAY %~ CEA-UPSAY-SACLAY %~ LIST %~ DM2I %~ GS-ENGINEERING %~ GS-COMPUTER-SCIENCE %~ DIN