Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data

Abstract : We look at different prognostic approaches and the way of quantifying confidence in equipment Remaining Useful Life (RUL) prediction. More specifically, we consider: (1) a particle filtering scheme, based on a physics-based model of the degradation process; (2) a bootstrapped ensemble of empirical models trained on a set of degradation observations measured on equipments similar to the one of interest; (3) a bootstrapped ensemble of empirical models trained on a sequence of past degradation observations from the equipment of interest only. The ability of these three approaches in providing measures of confidence for the RUL predictions is evaluated in the context of a simulated case study of interest in the nuclear power generation industry and concerning turbine blades affected by developing creeps. The main contribution of the work is the critical investigation of the capabilities of different prognostic approaches to deal with various sources of uncertainty in the RUL prediction.
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Reliability Engineering and System Safety, Elsevier, 2013, 112, pp.94-108. 〈10.1016/j.ress.2012.12.004〉
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Piero Baraldi, Francesca Mangili, Enrico Zio. Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data. Reliability Engineering and System Safety, Elsevier, 2013, 112, pp.94-108. 〈10.1016/j.ress.2012.12.004〉. 〈hal-00934547〉

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