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Fully Adaptive Particle Filtering Algorithm for Damage Diagnosis and Prognosis.

Elaheh Rabiei1,2, Enrique Lopez Droguett1,3, Mohammad Modarres1

  • 1Center for Risk and Reliability, Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA.

Entropy (Basel, Switzerland)
|December 3, 2020
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Summary
This summary is machine-generated.

This study introduces an adaptive particle filtering algorithm that updates both state and measurement models simultaneously for realistic online damage tracking. This method enhances accuracy in composite material fatigue damage estimation.

Keywords:
Kullback–Leibler divergenceadaptive measurement modelcomposite degradationcross entropy methoddiagnosis and prognosisfully adaptive particle filteringrelative entropy

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Area of Science:

  • Engineering
  • Materials Science
  • Computational Science

Background:

  • Existing Bayes filtering methods rely on fixed state and measurement models, limiting real-world applicability.
  • Online monitoring of damage requires adaptive models, especially for measurement correlations not defined a priori.
  • While state process model updates are explored, measurement model adaptation remains less studied.

Purpose of the Study:

  • To develop a fully adaptive particle filtering algorithm capable of simultaneous, separate updates of state and measurement models.
  • To enable more realistic online monitoring and tracking of damage, particularly when measurement models are not predefined.
  • To address the limitations of fixed models in dynamic systems with evolving measurement-state relationships.

Main Methods:

  • Proposed a novel adaptive particle filtering algorithm.
  • The algorithm optimizes relative entropy (Kullback-Leibler divergence) for model updates.
  • Employed simultaneous and separate updating of state process and measurement models.

Main Results:

  • Successfully applied the algorithm to a case study of online fatigue damage estimation in composite materials.
  • Demonstrated the capability of the adaptive algorithm to update both state and measurement models concurrently.
  • The approach offers a significant advancement for realistic online damage tracking.

Conclusions:

  • The developed adaptive particle filtering algorithm provides a robust solution for online damage monitoring.
  • Simultaneous updating of state and measurement models enhances the realism and accuracy of tracking.
  • This method is particularly valuable for applications where measurement models are not fixed or known in advance.