Abstract
A fully adaptive particle filtering algorithm is proposed in this paper which is capable of updating both state process models and measurement models separately and simultaneously. The approach is a significant step toward more realistic online monitoring or tracking damage. The majority of the existing methods for Bayes filtering are based on predefined and fixed state process and measurement models. Simultaneous estimation of both state and model parameters has gained attention in recent literature. Some works have been done on updating the state process model. However, not many studies exist regarding an update of the measurement model. In most of the real-world applications, the correlation between measurements and the hidden state of damage is not defined in advance and, therefore, presuming an offline fixed measurement model is not promising. The proposed approach is based on optimizing relative entropy or Kullback–Leibler divergence through a particle filtering algorithm. The proposed algorithm is successfully applied to a case study of online fatigue damage estimation in composite materials.
Highlights
The particle filtering (PF) approach has received significant attention recently as a very powerful and flexible tool for damage diagnosis and prognosis
The method is constructed on top of augmented PF, only for parameters of the state process model and adds the capability of adjusting the measurement model on the fly separately, based the concept of cross entropy as the foundation of the proposed fully adaptive particle filtering, in order to learn the parameters of the measurement model over time
This optimization problem is applied through the cross entropy method (CE method), which is rooted in the adaptive variance minimization algorithm for the rare event probability estimation in stochastic networks [44,45]
Summary
The particle filtering (PF) approach has received significant attention recently as a very powerful and flexible tool for damage diagnosis and prognosis. The approach would be a significant step forward to real-time degradation monitoring and life prediction It is useful for Structural Health Monitoring (SHM) in real-world applications because, when performing the damage monitoring and prognostics in real-time, both of the underlying state processes (degradation behavior) and measurement models (correlation between underlying degradation and condition-based monitoring data) are not fully defined in advance for the particular component/system under specific operating conditions. The method is constructed on top of augmented PF, only for parameters of the state process model and adds the capability of adjusting the measurement model on the fly separately, based the concept of cross entropy as the foundation of the proposed fully adaptive particle filtering, in order to learn the parameters of the measurement model over time.
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