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2007 Mediterranean Conference on Control & Automation
This paper presents the implementation of an online particle-filtering-based framework for fault diagnosis and failure prognosis in a turbine engine. The methodology considers two autonomous modules, and assumes the existence of fault indicators (for monitoring purposes) and the availability of real-time measurements. A fault detection and identification (FDI) module uses a hybrid state-space model of the plant, and a particle filtering algorithm to calculate the probability of a crack in onedoi:10.1109/med.2007.4433871 fatcat:4gi44tpphvb23m44ye6n6anguq