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Uncertainty quantification in system-level prognostics: application to Tennessee Eastman process

Abstract : This paper addresses the problem of uncertainty quantification in system-level prognostics. To this purpose, a three-step methodology, based on the inoperability input-output model, is presented. The first step concerns the estimation of the system inoperability, using a new adapted particle filtering method, while considering the interactions between its components. The second step focuses on the long-term prediction of the system inoperability in order to determine its evolution. Finally, in the third step, a method for calculating the remaining useful life of the system, based on the system configuration, is formulated. The proposed methodology is applied on data obtained from the Tennessee Eastman Process simulations to predict the shutdown due to violation of process constraints
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Submitted on : Friday, February 21, 2020 - 1:28:06 PM
Last modification on : Friday, October 2, 2020 - 3:06:03 PM
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  • HAL Id : hal-02487080, version 1
  • OATAO : 23821

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Ferhat Tamssaouet, Thi Phuong Khanh Nguyen, Kamal Medjaher, Marcos E. Orchard. Uncertainty quantification in system-level prognostics: application to Tennessee Eastman process. The sixth (6th) edition in the series of the International Conference on Control, Decision and Information Technologies - CODIT 2019, Apr 2019, Paris, France. pp.1-6. ⟨hal-02487080⟩

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