Particle Filter Based Monitoring and Prediction of Spatiotemporal Corrosion Using Successive Measurements of Structural Responses

Yi, Sang-ri; Song, Junho

doi:10.3390/s18113909

Cited by 8 publications

(2 citation statements)

References 33 publications

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“…, 50. In Figures 10,11,12 we plot the mean and credible intervals of these relative errors as obtained from 50 different runs. The off-line SMC filter, which does not provide the on-line solution within a single run, is run anew for estimating the single posterior density of interest at years 10, 20, 30, 40, 50, and in between, the relative error is linearly interpolated.…”

Section: Comparative Assessment Of the Investigated On-line And Off-l...mentioning

confidence: 99%

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

Kamariotis¹,

Sardi²,

Papaioannou³

et al. 2022

Preprint

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Predictive maintenance planning in the presence of structural deterioration largely relies on stochastic deterioration models, which typically contain time-invariant uncertain parameters. Monitoring information obtained sequentially at different points in time can be utilized to update prior knowledge on the timeinvariant parameters within the Bayesian framework. In sequential settings, Bayesian parameter estimation can be performed either in an off-line (batch) or an on-line (recursive) framework. With a focus on the quantification of the full parameter uncertainty, we review, discuss and investigate selected methods for Bayesian inference: 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 probabilistic fatigue crack growth model that is updated with sequential crack monitoring measurements. The second high-dimensional case study employs a random field to model the spatially and temporally varying 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. The investigated on-line particle filter proves competitive with MCMC-based filters. The effects of increasing problem dimensionality and sensor information amount on posterior estimates are demonstrated.

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Section: Comparative Assessment Of the Investigated On-line And Off-l...mentioning

confidence: 99%

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

Kamariotis¹,

Sardi²,

Papaioannou³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The deterioration models further contain time-invariant uncertain parameters. The state-space can be augmented to include these parameters, if one wishes to obtain updated estimates thereof conditional on the monitoring information (Saha et al, 2009;Straub, 2009;Sun et al, 2014;Corbetta et al, 2018;Yi and Song, 2018;Cristiani et al, 2021;Kamariotis et al, 2023); this is referred to as joint state-parameter estimation (Särkkä, 2013;Kantas et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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

Kamariotis

Sardi

Papaioannou

et al. 2023

DCE

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Data-informed predictive maintenance planning largely relies on stochastic deterioration models. Monitoring information can be utilized to update sequentially the knowledge on model parameters. In this context, on-line (recursive) Bayesian filtering algorithms typically fail to properly quantify the full posterior uncertainty of time-invariant model parameters. Off-line (batch) algorithms are—in principle—better suited for the uncertainty quantification task, yet they are computationally prohibitive in sequential settings. In this work, we 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 approximates the posterior distribution within the resampling process in all three filters. Two numerical examples provide the basis for a comparative assessment. The first example considers a low-dimensional, nonlinear, non-Gaussian probabilistic fatigue crack growth model that is updated with sequential monitoring measurements. The second high-dimensional, linear, Gaussian example employs a random field to model corrosion deterioration across a beam, which is updated with sequential sensor measurements. 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.

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Research of corrosion processes in body material of electrical dehydrator

Khasbutdinova

Naumkin

Elizariev

et al. 2020

J. Phys.: Conf. Ser.

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The task was set of determining the dependence of the quantitative indicator of the corrosion rate of the material of the body of the electric dehydrator on the composition of the aggressive medium, the development of measures to improve the quality of the assessment of workability and the assessment of the residual life, as well as ensuring the protection of the parts of the body of the electric dehydrator from exposure to the most aggressive working phases during operation. For research, rectangular samples were made of low-alloy steel 09G2S with dimensions of 20x20 mm and a thickness of 4 mm. To quantify corrosion wear, the samples were immersed for different times in three model media: ordinary water with different NaCl concentrations; the water, which has stood up from oil, with various concentration of NaCl; oil-water emulsion with an oil content in the water in the range from 7.7% to 95%. It was shown that the maximum corrosion wear is observed in a model medium, which is separated water with salt dissolved in it and characteristic of the lower bottom and lower zones of the electric dehydrator body.

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Particle Filter Based Monitoring and Prediction of Spatiotemporal Corrosion Using Successive Measurements of Structural Responses

Cited by 8 publications

References 33 publications

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

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

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

Research of corrosion processes in body material of electrical dehydrator

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