Atmospheric Corrosion Prognostics Using a Particle Filter

Luc S. Keizers*, Richard Loendersloot, Tiedo Tinga

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

A prognostic algorithm can guarantee high reliability and availability of machinery of structures at acceptable costs. This paper proposes to use a particle filter for atmospheric corrosion prognostics, which bridges the gap between corrosion modeling and corrosion monitoring. The applied particle filter only takes temperature and monitored mass loss as input and is based on Arrhenius equation. The output of the particle filter is a probability distribution of the remaining useful life that considers uncertainties on the process, the model and future weather conditions. The effectiveness of the approach is demonstrated by a case study composed from monthly exposure tests performed by the National Institute of Materials Science in Japan. It is shown that the particle filter estimates suitable model parameters of the corrosion model to give good remaining useful life estimations, while only requiring a relatively
simple corrosion model. In new practical applications challenges remain in parameter selection and initialization of the algorithm. Furthermore, the method should be validated on an actual long-term corrosion process.
Original languageEnglish
Title of host publicationProceedings of the 32nd European Safety and Reliability Conference (ESREL 2022)
Pages1259-1266
Number of pages8
ISBN (Electronic)978-981-18-5183-4
Publication statusPublished - 28 Aug 2022
Event32nd European Safety and Reliability Conference, ESREL 2022: Understanding and Managing Risk and Reliability for a Sustainable Future - Dublin, Ireland
Duration: 28 Aug 20221 Sept 2022
Conference number: 32

Conference

Conference32nd European Safety and Reliability Conference, ESREL 2022
Abbreviated titleESREL
Country/TerritoryIreland
CityDublin
Period28/08/221/09/22

Keywords

  • Predictive Maintenance
  • Corrosion
  • Prognostics
  • Varying Conditions
  • Particle Filter

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