Bayesian framework for probabilistic low cycle fatigue life prediction and uncertainty modeling of aircraft turbine disk alloys

Zhu, Shun‐Peng; Huang, Hong‐Zhong; Smith, Reuel; Ontiveros, Victor; He, Lei; Modarres, Mohammad

doi:10.1016/j.probengmech.2013.08.004

Cited by 109 publications

(56 citation statements)

References 38 publications

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“…, , . Bayesian method has widely been used in reliability research [9,11,14,16,24]. More details about Bayesian theory can be referred to O'Hagan et al [1,11,12].…”

Section: Illustrative Example and Parameter Estimationmentioning

confidence: 99%

A Bayesian approach for sealing failure analysis considering the non-competing relationship of multiple degradation processes

Yang¹,

Peng²,

Zhu³

et al. 2016

EiN

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“…, , . Bayesian method has widely been used in reliability research [9,11,14,16,24]. More details about Bayesian theory can be referred to O'Hagan et al [1,11,12].…”

Section: Illustrative Example and Parameter Estimationmentioning

confidence: 99%

A Bayesian approach for sealing failure analysis considering the non-competing relationship of multiple degradation processes

Yang¹,

Peng²,

Zhu³

et al. 2016

EiN

Self Cite

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“…In regard to fatigue life prediction of aircraft gas turbine, Zhu proposed a generalized energy-based fatigue-creep damage parameter [7], and developed some probabilistic prediction frameworks [8,9]. In addition, foreign object damage (FOD) will lead to a sharp shift of engine performance, which is called abrupt failure [10].…”

Section: Introductionmentioning

confidence: 99%

Performance Estimation and Fault Diagnosis Based on Levenberg–Marquardt Algorithm for a Turbofan Engine

Lü

Huang

2018

Energies

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Establishing the schemes of accurate and computationally efficient performance estimation and fault diagnosis for turbofan engines has become a new research focus and challenges. It is able to increase reliability and stability of turbofan engine and reduce the life cycle costs. Accurate estimation of turbofan engine performance counts on thoroughly understanding the components' performance, which is described by component characteristic maps and the fault of each component can be regarded as the change of characteristic maps. In this paper, a novel method based on a Levenberg-Marquardt (LM) algorithm is proposed to enhance the fidelity of the performance estimation and the credibility of the fault diagnosis for the turbofan engine. The presented method utilizes the LM algorithm to figure out the operating point in the characteristic maps, preparing for performance estimation and fault diagnosis. The accuracy of the proposed method is evaluated for estimating performance parameters in the transient case with Rayleigh process noise and Gaussian measurement noise. The comparison among the extended Kalman filter (EKF) method, the particle filter (PF) method and the proposed method is implemented in the abrupt fault case and the gradual degeneration case and it has been shown that the proposed method has the capability to lead to more accurate result for performance estimation and fault diagnosis of turbofan engine than current popular EKF and PF diagnosis methods.

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“…[1][2][3][4][5][6][7][8][9][10] The probability theory is one of the classical methods which is commonly used in RBMDO. Generally, it could be effective when sufficient uncertainty information is available and the probability distributions of random design variables can be obtained easily.…”

Section: Introductionmentioning

confidence: 99%

A concurrent reliability optimization procedure in the earlier design phases of complex engineering systems under epistemic uncertainties

Meng

Zhang²

2016

Advances in Mechanical Engineering

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Concurrent engineering has obtained increasing attention to solve the design problems of multidisciplinary systems. In practical engineering, there are epistemic uncertainties during whole design cycle of complex systems. Especially in earlier design phases, the effects of epistemic uncertainties are not usually easy to be quantified. It is because design information is insufficient. Furthermore, commonly used probability theory is also not suitable to be utilized. In this situation, epistemic uncertainties will be introduced unavoidably by mathematical models or simulation tools and may affect the performance of complex system significantly. To solve this problem, evidence theory is introduced and combined with the collaborative optimization method in this study. An evidence-based collaborative reliability optimization method is also proposed. Evidence theory is a powerful approach to handle epistemic uncertainties by Plausibility and Belief. Meanwhile, collaborative optimization is widely utilized in the concurrent design of complex systems. An aircraft conceptual design problem is utilized to show the application of the proposed method.

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Bayesian framework for probabilistic low cycle fatigue life prediction and uncertainty modeling of aircraft turbine disk alloys

Cited by 109 publications

References 38 publications

A Bayesian approach for sealing failure analysis considering the non-competing relationship of multiple degradation processes

A Bayesian approach for sealing failure analysis considering the non-competing relationship of multiple degradation processes

Performance Estimation and Fault Diagnosis Based on Levenberg–Marquardt Algorithm for a Turbofan Engine

A concurrent reliability optimization procedure in the earlier design phases of complex engineering systems under epistemic uncertainties

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