Intelligent moving extremum weighted surrogate modeling framework for dynamic reliability estimation of complex structures

Teng, Da; Feng, Yunwen; Chen, Jun-Yu

doi:10.1016/j.engfailanal.2022.106364

Cited by 20 publications

(2 citation statements)

References 28 publications

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“…Yu et al developed the adaptive Kriging and weighted sampling for reliability assessment of engineering structures [21]. Teng et al presented the weighted Kriging model to perform the reliability design of turbine blisk [22]. Roy et al investigated the application of SVM in structural reliability analysis [23].…”

Section: Introductionmentioning

confidence: 99%

Operational Reliability Analysis of Turbine Blisk Using an Enhanced Moving Neural Network Framework

Liang,

Sun,

Sun

et al. 2024

Aerospace

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As one of the key components of an aeroengine, turbine blisk endures complex coupling loads under a harsh operational environment so that the reliability of turbine blisk directly influences the safe operation of aeroengine. It is urgent to precisely perform the reliability estimation of a complex blisk structure. To address this issue, an enhanced Moving Neural Network Framework (MNNF) is proposed by integrating compact support region theory, improve sooty tern optimization algorithm (ISTOA), and Bayesian regularization strategy into artificial neural network. The compact support region theory is applied to select the efficient samples for modeling from the training samples set, the ISTOA is to determine the optimal compact support region, and Bayesian regularization thought is utilized to improve the generalization ability of neural network model. The operational reliability assessment of aeroengine blisk is performed with the consideration of transient loads to verify the proposed MNNF method. It is shown that the reliability degree of turbine blisk stain is 0.9984 when the allowable value is 5.2862 × 10−3 m. In line with the comparison of methods, the developed MNNF approach has 0.99738 in root means square error, 3.1634 × 10−4 m in goodness of fit, 0.423 s in modeling time, 99.99% in simulation precision, and 0.496 s in simulation time under 10,000 simulations, which are superior to all other methods (i.e., 99.96%, 99.91%, 99.93%, 99.97%, and 99.97% in simulation precision and 16.27%, 4.82%, 30.07%, 39.87%, and 23.59% in simulation efficiency, for the response surface method (RSM), Kriging, support vector machine (SVM), back propagation-artificial neural network (BP-NN), and BP-NN based on particle swarm optimization (BP-PSO) methods, respectively). It is demonstrated that the MNNF method holds excellent modeling and simulation performances. The efforts of this study provide promising tools and insights into the reliability design of complex structures, and enrich and develop reliability theory.

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Section: Introductionmentioning

confidence: 99%

Operational Reliability Analysis of Turbine Blisk Using an Enhanced Moving Neural Network Framework

Liang,

Sun,

Sun

et al. 2024

Aerospace

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“…stress, deformation, strain and so forth) is determined by multiple dynamic deterministic analyses based on the material property, structure loads and dimensions, and the samples of input variables and output responses are obtained. Then the limit state function is approximated by the response surface method (RSM) (Lu et al, 2020;Zhang et al, 2017;Kaymaz and McMahon, 2005), support vector regression (SVM) (Feng et al, 2019;Chen et al, 2022;Hariri-Ardebili and Pourkamali-Anaraki, 2018;Keshtegar et al, 2021), artificial neural network (ANN) (Li et al, 2021;Cherid et al, 2021;Peng et al, 2019) and Kriging (Teng et al, 2022;Zhang et al, 2021a, b;Jiang et al, 2019a, b) surrogate model, and the reliability is analyzed by combining the allowable value. Zhang and Bai (2012) presented the extremum RSM used for the reliability analysis of a two-link flexible robot manipulator.…”

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confidence: 99%

Structural dynamic reliability analysis: review and prospects

Teng

Feng

Chen

et al. 2022

IJSI

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PurposeThe purpose of this paper is to briefly summarize and review the theories and methods of complex structures’ dynamic reliability. Complex structures are usually assembled from multiple components and subjected to time-varying loads of aerodynamic, structural, thermal and other physical fields; its reliability analysis is of great significance to ensure the safe operation of large-scale equipment such as aviation and machinery.Design/methodology/approachIn this paper for the single-objective dynamic reliability analysis of complex structures, the calculation can be categorized into Monte Carlo (MC), outcrossing rate, envelope functions and extreme value methods. The series-parallel and expansion methods, multi-extremum surrogate models and decomposed-coordinated surrogate models are summarized for the multiobjective dynamic reliability analysis of complex structures.FindingsThe numerical complex compound function and turbine blisk are used as examples to illustrate the performance of single-objective and multiobjective dynamic reliability analysis methods. Then the future development direction of dynamic reliability analysis of complex structures is prospected.Originality/valueThe paper provides a useful reference for further theoretical research and engineering application.

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A unified reliability evaluation framework for aircraft turbine rotor considering multi-site failure correlation

Huang

Zhang

Song

et al. 2023

Struct Multidisc Optim

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Intelligent moving extremum weighted surrogate modeling framework for dynamic reliability estimation of complex structures

Cited by 20 publications

References 28 publications

Operational Reliability Analysis of Turbine Blisk Using an Enhanced Moving Neural Network Framework

Operational Reliability Analysis of Turbine Blisk Using an Enhanced Moving Neural Network Framework

Structural dynamic reliability analysis: review and prospects

A unified reliability evaluation framework for aircraft turbine rotor considering multi-site failure correlation

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