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Statistical analysis and reliability prediction with short fatigue crack data
Author(s) -
Wilson S.P.,
Taylor D.
Publication year - 1999
Publication title -
fatigue and fracture of engineering materials and structures
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.887
H-Index - 84
eISSN - 1460-2695
pISSN - 8756-758X
DOI - 10.1046/j.1460-2695.1999.00140.x
Subject(s) - statistical inference , bayesian inference , reliability (semiconductor) , monte carlo method , inference , statistical model , bayesian probability , population , paris' law , fracture mechanics , structural engineering , computer science , mathematics , statistics , engineering , artificial intelligence , crack closure , physics , power (physics) , demography , quantum mechanics , sociology
This paper proposes a probability model to describe the growth of short fatigue cracks. The model defines the length of each crack in a specimen as a random quantity, which is a function of randomly varying local properties of the material microstructure. Once the model has been described, the paper addresses two questions: first, statistical inference, i.e. the fitting of the model parameters to data on crack lengths; and secondly, predicting the future behaviour of observed cracks or cracks in a new specimen. By defining failure of a specimen to be the time at which the largest crack exceeds a certain length, the solution to the prediction problem can be used to calculate a probability that the specimen has failed at any future time. The probability model for crack lengths is called a population model, and the statistical inference uses the ideas of Bayesian statistics. Both these concepts are described. With a population model, the solution to statistical inference and prediction requires quite complicated Monte Carlo simulation techniques, which are also described.