Techno Press


Structural Engineering and Mechanics   Volume 37, Number 4, February25 2011, pages 427-442
DOI: http://dx.doi.org/10.12989/sem.2011.37.4.427
 
Fatigue life prediction based on Bayesian approach to incorporate field data into probability model
Dawn An, Joo-Ho Choi, Nam H. Kim and Sriram Pattabhiraman

 
Abstract     [Full Text]
    In fatigue life design of mechanical components, uncertainties arising from materials and manufacturing processes should be taken into account for ensuring reliability. A common practice is to apply a safety factor in conjunction with a physics model for evaluating the lifecycle, which most likely relies on the designer\'s experience. Due to conservative design, predictions are often in disagreement with field observations, which makes it difficult to schedule maintenance. In this paper, the Bayesian technique, which incorporates the field failure data into prior knowledge, is used to obtain a more dependable prediction of fatigue life. The effects of prior knowledge, noise in data, and bias in measurements on the distribution of fatigue life are discussed in detail. By assuming a distribution type of fatigue life, its parameters are identified first, followed by estimating the distribution of fatigue life, which represents the degree of belief of the fatigue life conditional to the observed data. As more data are provided, the values will be updated to reduce the credible interval. The results can be used in various needs such as a risk analysis, reliability based design optimization, maintenance scheduling, or validation of reliability analysis codes. In order to obtain the posterior distribution, the Markov Chain Monte Carlo technique is employed, which is a modern statistical computational method which effectively draws the samples of the given distribution. Field data of turbine components are exploited to illustrate our approach, which counts as a regular inspection of the number of failed blades in a turbine disk.
 
Key Words
    fatigue life; prior distribution; posterior distribution; Bayesian approach; Markov Chain Monte Carlo Technique; field inspection; turbine blade.
 
Address
Dawn An: School of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-City, Korea
Joo-Ho Choi: School of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-City, Korea
Nam H. Kim: Dept. of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL, USA
Sriram Pattabhiraman: Dept. of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL, USA
 

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