The analysis of initial probability distribution in Markov Chain model for lifetime estimation

Research output: Contribution to journalArticle

Abstract

This paper presents the analysis and modeling of predicting fatigue lifetime based on the Markov Chain model. Random factor is the main contributor to the prediction of fatigue lifetime. These random factors give an appropriate framework for modeling and predicting a lifetime of the structure. The Markov Chain model was used to predict the probability of fatigue lifetime based on the randomization of initial probability distribution. An approach of calculating the initial probability distribution is introduced based on the statistical distribution of initial crack length and the transition probability was formed using a classical deterministic Paris law. The classical Paris law has been used in calculating the transition probabilities matrix to represent the physical meaning of fatigue crack growth problem. The data from the experimental work under constant amplitude loading was analyzed using the Markov Chain model. The results show that the model is capable to predict the fatigue lifetime for Aluminum Alloy A7075-T6.

Original languageEnglish
Pages (from-to)44-48
Number of pages5
JournalInternational Journal of Integrated Engineering
Volume10
Issue number5
DOIs
Publication statusPublished - 1 Jan 2018

Fingerprint

Markov processes
Probability distributions
Fatigue of materials
Fatigue crack propagation
Aluminum alloys
Cracks

Keywords

  • Fatigue lifetime
  • Markov Chain model
  • Paris law equation
  • Probability distribution

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Materials Science (miscellaneous)
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

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title = "The analysis of initial probability distribution in Markov Chain model for lifetime estimation",
abstract = "This paper presents the analysis and modeling of predicting fatigue lifetime based on the Markov Chain model. Random factor is the main contributor to the prediction of fatigue lifetime. These random factors give an appropriate framework for modeling and predicting a lifetime of the structure. The Markov Chain model was used to predict the probability of fatigue lifetime based on the randomization of initial probability distribution. An approach of calculating the initial probability distribution is introduced based on the statistical distribution of initial crack length and the transition probability was formed using a classical deterministic Paris law. The classical Paris law has been used in calculating the transition probabilities matrix to represent the physical meaning of fatigue crack growth problem. The data from the experimental work under constant amplitude loading was analyzed using the Markov Chain model. The results show that the model is capable to predict the fatigue lifetime for Aluminum Alloy A7075-T6.",
keywords = "Fatigue lifetime, Markov Chain model, Paris law equation, Probability distribution",
author = "Januri, {Siti Sarah} and {Mohd Nopiah}, Zulkifli and {Mohd Ihsan}, {Ahmad Kamal Ariffin} and Nurulkamal Masseran and Shahrum Abdullah",
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AU - Mohd Nopiah, Zulkifli

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AU - Abdullah, Shahrum

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N2 - This paper presents the analysis and modeling of predicting fatigue lifetime based on the Markov Chain model. Random factor is the main contributor to the prediction of fatigue lifetime. These random factors give an appropriate framework for modeling and predicting a lifetime of the structure. The Markov Chain model was used to predict the probability of fatigue lifetime based on the randomization of initial probability distribution. An approach of calculating the initial probability distribution is introduced based on the statistical distribution of initial crack length and the transition probability was formed using a classical deterministic Paris law. The classical Paris law has been used in calculating the transition probabilities matrix to represent the physical meaning of fatigue crack growth problem. The data from the experimental work under constant amplitude loading was analyzed using the Markov Chain model. The results show that the model is capable to predict the fatigue lifetime for Aluminum Alloy A7075-T6.

AB - This paper presents the analysis and modeling of predicting fatigue lifetime based on the Markov Chain model. Random factor is the main contributor to the prediction of fatigue lifetime. These random factors give an appropriate framework for modeling and predicting a lifetime of the structure. The Markov Chain model was used to predict the probability of fatigue lifetime based on the randomization of initial probability distribution. An approach of calculating the initial probability distribution is introduced based on the statistical distribution of initial crack length and the transition probability was formed using a classical deterministic Paris law. The classical Paris law has been used in calculating the transition probabilities matrix to represent the physical meaning of fatigue crack growth problem. The data from the experimental work under constant amplitude loading was analyzed using the Markov Chain model. The results show that the model is capable to predict the fatigue lifetime for Aluminum Alloy A7075-T6.

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KW - Markov Chain model

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