Considering traditional model-based prognostics approaches, a previously defined model is required to estimate the system’s health state and then propagate it to predict the system remaining useful life (SRUL). Following a Bayesian framework, the result of this prior estimation is updated by in-field measurements without changing the model parameters. Nevertheless, in the case of prognostics at system-level, solely updating prior health state, based on the pre-determined model, is no longer sufficient because numerous mutual interactions between components cause multiple uncertainties in system degradation modeling, and then can lead to inaccurate SRUL prediction. Therefore, this paper proposes a new methodology for online joint uncertainty quantification and model estimation based on particle filtering (PF) and gradient descent (GD). In detail, the inoperability input-output model (IIM) is used to characterize system degradations considering interactions between components and effects of the mission profile; and then the inoperability of system components is estimated in a probabilistic manner using PF. In the case of consecutive discrepancy between the prior and posterior estimates of the system health state, GD is used to correct and to adapt the IIM parameters. To illustrate the effectiveness of the proposed methodology and its suitability for an online implementation, the Tennessee Eastman Process is investigated as a case study.
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System-level prognostics ;, Online failure prognostics, Adaptive degradation model, Uncertainty quantification
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