Stochastic modeling of fatigue crack damage for information-based maintenance

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

The concept of information-based maintenance is that of updating decisions for inspection, repair, and maintenance scheduling based on evolving knowledge of operation history and anticipated usage of the machinery as well as the physics and dynamics of material degradation in critical components. This paper presents a stochastic model of fatigue crack damage in metallic structures for application to information-based maintenance of operating machinery. The information on operation history allows the stochastic model to predict the current state of damage, and the information on anticipated usage of the machinery facilitates forecasting the remaining service life based on the stress level to which the critical components are likely to be subjected. The Karhunen-Loève expansion for nonstationary processes is utilized for formulating the stochastic model which generates the crack length statistics in the setting of a two-parameter lognormal distribution. Hypothesis tests are built upon the (conditional) probability density function of crack damage that does not require the solution of stochastic differential equations in either Wiener integral or Itô integral settings. Consequently, structural damage and remaining life of stressed components can be predicted to make maintenance decisions in real time. The damage model is verified by comparison with experimental data of fatigue crack statistics for 2024-T3 and 7075-T6 aluminum alloys. Examples are presented to demonstrate how this concept can be applied to hypothesis testing and remaining life prediction.

Original languageEnglish (US)
Pages (from-to)191-204
Number of pages14
JournalAnnals of Operations Research
Volume91
StatePublished - Dec 1 1999

Fingerprint

Fatigue
Stochastic modeling
Damage
Machinery
Stochastic model
Statistics
Integral
Prediction
Physics
Degradation
Hypothesis testing
Aluminum
Repair
Hypothesis test
Conditional probability
Log normal distribution
Nonstationary processes
Inspection
Probability density function
Stochastic differential equations

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Management Science and Operations Research

Cite this

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title = "Stochastic modeling of fatigue crack damage for information-based maintenance",
abstract = "The concept of information-based maintenance is that of updating decisions for inspection, repair, and maintenance scheduling based on evolving knowledge of operation history and anticipated usage of the machinery as well as the physics and dynamics of material degradation in critical components. This paper presents a stochastic model of fatigue crack damage in metallic structures for application to information-based maintenance of operating machinery. The information on operation history allows the stochastic model to predict the current state of damage, and the information on anticipated usage of the machinery facilitates forecasting the remaining service life based on the stress level to which the critical components are likely to be subjected. The Karhunen-Lo{\`e}ve expansion for nonstationary processes is utilized for formulating the stochastic model which generates the crack length statistics in the setting of a two-parameter lognormal distribution. Hypothesis tests are built upon the (conditional) probability density function of crack damage that does not require the solution of stochastic differential equations in either Wiener integral or It{\^o} integral settings. Consequently, structural damage and remaining life of stressed components can be predicted to make maintenance decisions in real time. The damage model is verified by comparison with experimental data of fatigue crack statistics for 2024-T3 and 7075-T6 aluminum alloys. Examples are presented to demonstrate how this concept can be applied to hypothesis testing and remaining life prediction.",
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Stochastic modeling of fatigue crack damage for information-based maintenance. / Ray, Asok; Phoha, Shashi.

In: Annals of Operations Research, Vol. 91, 01.12.1999, p. 191-204.

Research output: Contribution to journalArticle

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