Online fuzzy temporal operators for complex system monitoring

Jean-Philippe Poli (1) , Laurence Boudet (1) , Bruno Espinosa (1) , Laurence Cornez (2)

1 LI3A - Intelligence Artificielle et Apprentissage Automatique
2 LS2D - Laboratoire Sciences des Données et de la Décision

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.

Keywords: System monitoring; Natural languages; Mathematical definitions; High-level information; Fuzzy expert systems; Event streams; Online systems; Large scale systems; Expert systems; Temporal operators; Temporal relation; Mathematical operators; Computer hardware description languages; online learning; machine learning; artificial intelligence; fuzzy logic

Domains: Artificial Intelligence [cs.AI]; Data Analysis, Statistics and Probability [physics.data-an]; Information Retrieval [cs.IR]

Jean-Philippe Poli, Laurence Boudet, Bruno Espinosa, Laurence Cornez. Online fuzzy temporal operators for complex system monitoring. ISIPTA '17 and ECSQARU 2017 - 14th European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, Jul 2017, Lugano, Switzerland. pp.375-384, ⟨10.1007/978-3-319-61581-3_34⟩. ⟨cea-01809219⟩