On off-line and on-line Bayesian filtering for uncertainty quantification of structural deterioration

05/06/2022
by   Antonios Kamariotis, et al.
0

Data-informed predictive maintenance planning largely relies on stochastic deterioration models. Monitoring information can be utilized to update sequentially the knowledge on time-invariant deterioration model parameters either within an off-line (batch) or an on-line (recursive) Bayesian framework. With a focus on the quantification of the full parameter uncertainty, we review, adapt and investigate selected Bayesian filters for parameter estimation: an on-line particle filter, an on-line iterated batch importance sampling filter, which performs Markov chain Monte Carlo (MCMC) move steps, and an off-line MCMC-based sequential Monte Carlo filter. A Gaussian mixture model is used to approximate the posterior distribution within the resampling process in all three filters. Two numerical examples serve as the basis for a comparative assessment of off-line and on-line Bayesian estimation of time-invariant deterioration model parameters. The first case study considers a low-dimensional, nonlinear, non-Gaussian probabilistic fatigue crack growth model that is updated with sequential crack monitoring measurements. The second high-dimensional, linear, Gaussian case study employs a random field to model corrosion deterioration across a beam, which is updated with sequential measurements from sensors. The numerical investigations provide insights into the performance of off-line and on-line filters in terms of the accuracy of posterior estimates and the computational cost, when applied to problems of different nature, increasing dimensionality and varying sensor information amount. Importantly, they show that a tailored implementation of the on-line particle filter proves competitive with the computationally demanding MCMC-based filters. Suggestions on the choice of the appropriate method in function of problem characteristics are provided.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/26/2017

Particle rolling MCMC with double block sampling: conditional SMC update approach

An efficient simulation-based methodology is proposed for the rolling wi...
research
07/12/2013

On-line Bayesian parameter estimation in general non-linear state-space models: A tutorial and new results

On-line estimation plays an important role in process control and monito...
research
07/23/2018

A practical example for the non-linear Bayesian filtering of model parameters

In this tutorial we consider the non-linear Bayesian filtering of static...
research
10/26/2020

Bayesian Fusion of Data Partitioned Particle Estimates

We present a Bayesian data fusion method to approximate a posterior dist...
research
01/20/2021

Evaluating uncertainties in electrochemical impedance spectra of solid oxide fuel cells

Electrochemical impedance spectroscopy (EIS) is a widely used tool for c...
research
10/23/2022

Online Probabilistic Model Identification using Adaptive Recursive MCMC

The Bayesian paradigm provides a rigorous framework for estimating the w...
research
08/30/2023

Sequential Bayesian Predictive Synthesis

Dynamic Bayesian predictive synthesis is a formal approach to coherently...

Please sign up or login with your details

Forgot password? Click here to reset