A recursive Bayesian approach for fatigue damage prognosis: An experimental validation at the reliability component level

Gobbato, Maurizio; Kosmatka, J. B.; Conte, Joseph Le

doi:10.1016/j.ymssp.2013.10.014

Cited by 37 publications

(35 citation statements)

References 17 publications

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“…Similarly to the studies presented by Sun et al (2014) and Gobbato et al (2014), we employ the widely adopted Paris law in fracture mechanics as the state model to describe the physics of crack propagation (or state transition), and a simple random walk model as the measurement model.…”

Section: Prognosis Of Crack Length Using Ekf On Virkler Datamentioning

confidence: 99%

“…This paper presents a scheme of applying the EKF-based Bayesian probabilistic model to the Virkler data in order to predict crack length for any given number of cycles. Similar to the studies by Sun et al (2014) and Gobbato et al (2014), we employ the widely adopted Paris law in fracture mechanics as the state model to describe the physics of crack propagation (or state transition), and a simple random walk model as the measurement model. Instead of using a multiplicative lognormal variable to randomize the Paris model, like Spencer et al (1989) and Ray et al (1996), we employ a normally distributed Paris constant C, while keeping the Paris exponent m fixed to obtain a much simpler state transition process equation.…”

Section: Introductionmentioning

confidence: 99%

“…Using the recursive EKF solution, we are able to achieve very promising prognosis results in terms of the accuracy of prediction, and demonstrate the robustness of the method in dealing with uncertainties in the parameters defining the Paris law and the uncertainties in the measurements. Compared to other methods in the literature, the EKF-based approach in this paper is much simpler in problem formulation (thus much lower computational cost) than those by Spencer et al (1989), Ray et al (1996), Sun et al (2014) and Gobbato et al (2014) and should be more robust than the method by Ray et al (1996). In addition, the method uses a state transition process equation that is more readily adaptable for real-time PHM systems.…”

Section: Introductionmentioning

confidence: 99%

“…This approach has an advantage over the method used by Spencer et al (1989) in terms of computational efficiency, but there are a couple of stress-() and environment-(z 0 ) dependent parameters in the model that need to be tuned largely by trial and error, which would be sensitive to noise in stress and environment and could lead to a lack of robustness. Recently, Sun et al (2014) and Gobbato et al (2014) separately reported studies to estimate the remaining fatigue life given the current crack length. These studies used the Virkler data and solved the non-Gaussian state space model with a Particle Markov Chain Monte Carlo (PMCMC) method (a purely numerical method), and a recursive Bayesian approach (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Fatigue crack prognosis using Bayesian probabilistic modelling

Wang

Armstrong

2017

Mechanical Engineering Journal

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Prognosis of fatigue crack growth for mechanical and structural components is vital for aging military aircraft operated near or beyond their original design lives. For modern aircraft, prognostics and health management is supposed to be a designed-in capability; however, prognosis of mechanical and structural damage is yet to fully mature. This paper presents a scheme adopting Bayesian probabilistic modelling, extended Kalman filter (EKF) in particular, to predict fatigue crack growth in a common aircraft structural material: 2024-T3 aluminum alloy. In this scheme, the state model is the widely adopted Paris law in fracture mechanics (used to model the physics of crack growth), and the measurement model is a simple random walk model. The scheme is validated using a set of published crack growth test data, often referred to as the Virkler data, where the state model parameters are derived from one half of the data and the crack length prediction is made on the other half of the data. The EKF framework is further validated using a set of gear tooth crack propagation test data, where the crack length is the unobservable (or hidden) state variable, and the observable variable is a feature extracted from the gear vibration signal. The state model is also derived from the Paris law and the measurement model is developed using the observed relationship between the known crack length, the applied stress, and the energy of the impulsive signature extracted from an optimized sinusoidal model for gear vibration signals. Using the recursive EKF solution, we are able to achieve promising prognostic results in terms of the accuracy of the prediction, and demonstrate the method's robustness in dealing with uncertainties in the parameters defining the Paris law and the uncertainties in the measurements. Compared to other studies, the proposed method is a much simpler and more robust approach to the prognosis of fatigue crack size in mechanical structures and rotating components.

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Section: Prognosis Of Crack Length Using Ekf On Virkler Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fatigue crack prognosis using Bayesian probabilistic modelling

Wang

Armstrong

2017

Mechanical Engineering Journal

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“…In particular, the Bayesian approach has been successfully applied for uncertainty quantification in fatigue, but mainly in the context of metals [12][13][14][15][16][17][18][19]. For example, Cross et al [13] and Sankararaman et al [14] used Bayesian inference to estimate parameters underlying crack growth behavior.…”

Section: Introductionmentioning

confidence: 99%

Bayesian model selection and parameter estimation for fatigue damage progression models in composites

Chiachío

Saxena

et al. 2015

International Journal of Fatigue

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a b s t r a c tA Bayesian approach is presented for selecting the most probable model class among a set of damage mechanics models for fatigue damage progression in composites. Candidate models, that are first parameterized through a Global Sensitivity Analysis, are ranked based on estimated probabilities that measure the extent of agreement of their predictions with observed data. A case study is presented using multiscale fatigue damage data from a cross-ply carbon-epoxy laminate. The results show that, for this case, the most probable model class among the competing candidates is the one that involves the simplest damage mechanics. The principle of Ockham's razor seems to hold true for the composite materials investigated here since the data-fit of more complex models is penalized, as they extract more information from the data.

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