Application of stochastic filtering for lifetime prediction

Eija Myötyri (Corresponding Author), Urho Pulkkinen, Kaisa Simola

Research output: Contribution to journalArticleScientificpeer-review

86 Citations (Scopus)

Abstract

This paper introduces a stochastic filtering modeling approach for predicting the remaining lifetime of a component based on information on the stochastic degradation process and uncertain condition monitoring measurements. The model is illustrated by a case study, where the degradation is assumed to be a simplified fatigue crack growth process. The model accounts for uncertainties in both degradation process and condition measurements in a sound way. If completed with information on costs of monitoring, failure and replacement, such model could be used in optimizing both the condition monitoring intervals and, e.g. the replacement time for the component.
Original languageEnglish
Pages (from-to)200-208
JournalReliability Engineering and System Safety
Volume91
Issue number2
DOIs
Publication statusPublished - 2005
MoE publication typeA1 Journal article-refereed

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Condition monitoring
Degradation
Fatigue crack propagation
Acoustic waves
Monitoring
Costs
Uncertainty

Keywords

  • stochastic filtering
  • lifetime
  • fatigue crack growth
  • condition monitoring

Cite this

Myötyri, Eija ; Pulkkinen, Urho ; Simola, Kaisa. / Application of stochastic filtering for lifetime prediction. In: Reliability Engineering and System Safety. 2005 ; Vol. 91, No. 2. pp. 200-208.
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Application of stochastic filtering for lifetime prediction. / Myötyri, Eija (Corresponding Author); Pulkkinen, Urho; Simola, Kaisa.

In: Reliability Engineering and System Safety, Vol. 91, No. 2, 2005, p. 200-208.

Research output: Contribution to journalArticleScientificpeer-review

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AB - This paper introduces a stochastic filtering modeling approach for predicting the remaining lifetime of a component based on information on the stochastic degradation process and uncertain condition monitoring measurements. The model is illustrated by a case study, where the degradation is assumed to be a simplified fatigue crack growth process. The model accounts for uncertainties in both degradation process and condition measurements in a sound way. If completed with information on costs of monitoring, failure and replacement, such model could be used in optimizing both the condition monitoring intervals and, e.g. the replacement time for the component.

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DO - 10.1016/j.ress.2005.01.002

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