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

• Published in: International Journal of Integrated Engineering
• Main Author: Januri S.S.; Nopiah Z.M.; Ihsan A.K.A.M.; Masseran N.; Abdullah S.
• Format: Article
• Language: English
• Published: Penerbit UTHM 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061651538&doi=10.30880%2fijie.2018.10.05.008&partnerID=40&md5=f0c2bc2d7693d146fa2055981b5fda83

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. © 2018, Penerbit UTHM.