Bayesian model updating and prognosis of fatigue crack growth

Zárate, Boris A.; Caicedo, Juan M.; Yu, Jing; Ziehl, Paul

doi:10.1016/j.engstruct.2012.06.012

Cited by 50 publications

(30 citation statements)

References 38 publications

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“…For components and structures undergoing FCG, several researchers [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] have developed methodologies to ascertain the fatigue life. Karandikar et al 4 have applied Bayesian inference using a random walk method to predict the remaining useful life of an aircraft fuselage panel.…”

Section: A Brief Review Of the Related Literaturementioning

confidence: 99%

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Fatigue crack growth prediction in nuclear piping using Markov chain Monte Carlo simulation

Rastogi

Ghosh

et al. 2016

Fatigue Fract Eng Mat Struct

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In this paper, we present and demonstrate a methodology to improve probabilistic fatigue crack growth (FCG) predictions by using the concept of Bayesian updating using Markov chain Monte Carlo simulations. The methodology is demonstrated on a cracked pipe undergoing fatigue loading. Initial estimates of the FCG rate are made using the Paris law. The prior probability distributions of the Paris law parameters are taken from the tests on specimen made of the same material as that of pipe. Measured data on crack depth over number of loading cycles are used to update the prior distribution using the Markov chain Monte Carlo. The confidence interval on the predicted FCG rate is also estimated. In actual piping placed in a plant, the measured data can be considered equivalent to the data received from in‐service inspection. It is shown that the proposed methodology improves the fatigue life prediction. The number of observations used for updating is found to leave a significant effect on the accuracy of the updated prediction.

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Section: A Brief Review Of the Related Literaturementioning

confidence: 99%

“…Markov chains Monte Carlo-based methods have also been proposed in the literature [14][15][16][17][18] to address the uncertainty in FCG estimation. An et al 19 in their paper have estimated the distribution of the fatigue lifetime of turbine blades using the MCMC method.…”

Section: A Brief Review Of the Related Literaturementioning

confidence: 99%

Fatigue crack growth prediction in nuclear piping using Markov chain Monte Carlo simulation

Rastogi

Ghosh

et al. 2016

Fatigue Fract Eng Mat Struct

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show abstract

“…Similarly, inspection scheduling and short-term routing have also been optimized using updated reliability frameworks [9,10]. In a similar vein, Zrate et al [11] presented a framework to update and predict crack length as a function of the number of cycles in structural elements subjected to fatigue. Meulen and Hageman [12] used Bayesian analysis to predict fatigue accumulation using data from Fatigue Damage Sensors (FDS) a notched sensing foil which records site-specific fatigue damage.…”

Section: Introductionmentioning

confidence: 99%

Fusing fleet in-service measurements using Bayesian networks

Groden

Collette

2017

Marine Structures

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“…Many investigations were conducted to reduce the uncertainties of the deterioration process in structure reliability analysis and health monitoring [34,35]. An et al [36] studied the influence of noise and bias in updating the parameters of Paris law based model.…”

Section: Introductionmentioning

confidence: 99%

Bearing Remaining Useful Life Prediction Based on a Nonlinear Wiener Process Model

Wen

Gao

Zhang

2018

Shock and Vibration

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Prognostic is an essential part of condition-based maintenance, which can be employed to enhance the reliability and availability and reduce the maintenance cost of mechanical systems. This paper develops an improved remaining useful life (RUL) prediction method for bearings based on a nonlinear Wiener process model. First, the service life of bearings is divided into two stages in terms of the working condition. Then a new prognostic model is constructed to reflect the relationship between time and bearing health status. Besides, a variety of factors that cause uncertainties toward the degradation path are considered and appropriately managed to obtain reliable RUL prediction results. The particle filtering is utilized to estimate the degradation state, qualify the uncertainties, and predict the RUL. The experimental studies show that the proposed method has a better performance in RUL prediction and uncertainty management than the exponential model and the linear model.

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Bayesian model updating and prognosis of fatigue crack growth

Cited by 50 publications

References 38 publications

Fatigue crack growth prediction in nuclear piping using Markov chain Monte Carlo simulation

Fatigue crack growth prediction in nuclear piping using Markov chain Monte Carlo simulation

Fusing fleet in-service measurements using Bayesian networks

Bearing Remaining Useful Life Prediction Based on a Nonlinear Wiener Process Model

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