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  1. research.tudelft.nl

    In this context, this paper proposes a scheme of online fusing multiple models in a particle filter (PF)-based damage prognosis framework. First, each prognos-tic model has its process equation built through a physics-based or data-driven degradation model and has its measure-ment equation linking the damage state and the measurement.
  2. semanticscholar.org

    Nov 1, 2023Semantic Scholar extracted view of "Particle filter-based damage prognosis using online feature fusion and selection" by Tian-mei Li et al.
  3. ui.adsabs.harvard.edu

    Damage prognosis generally resorts to damage quantification functions and evolution models to quantify the current damage state and to predict the future states and the remaining useful life (RUL). The former typically consists of a function describing the relationship between the damage state and a statistical feature extracted from the measured signals, thus the prognostic performance will ...
  4. sciencedirect.com

    As a properly defined damage-sensitive statistical feature extracted from the Lamb waves can be used to online quantify the damage state, it is here exploited within a particle filter (PF) scheme to perform damage prognosis in structural health monitoring.
  5. sciencedirect.com

    Apr 1, 2024In this context, this paper proposes a scheme of prognostic-aided model updating in a particle filter (PF)-based prognostic framework, which empowers the updated model to capture both the historical and future degradation process, and consequently, enhances the prognostic performance.
  6. onlinelibrary.wiley.com

    Particle filter (PF) serves as the estimation technique to identify the state and parameters relating to the damage as well as the bias parameter, and the remaining useful life (RUL) can be predicted by the physics-based process equation, with PF posterior estimates of the related parameters and state variables as an input.
  7. ouci.dntb.gov.ua

    Liu, Combined parameter and state estimation in simulation-based filtering, Sequential Monte Carlo methods in practice, с. 197 Chatzi, The unscented Kalman filter and particle filter methods for nonlinear structural system identification with non-collocated heterogeneous sensing, Struct.
  8. researchgate.net

    Fig. 1 presents the four main steps of the proposed damage prognosis framework, namely, (i) formulate some independent prognostic models, each with one associated observed feature [6,7], (ii ...
  9. re.public.polimi.it

    Particle filter-based damage prognosis framework generally has three main steps, namely, (i) formulating a state space model, (ii) estimating the unknown state components using PF and (iii) calculating the future state and RUL by the PF estimates and the physic-based process equation.

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