Active Kriging-Based Adaptive Importance Sampling for Reliability and燬ensitivity Analyses of Stator Blade Regulator

Zhang, Hong; Song, Lu-Kai; Bai, Guang-Chen

doi:10.32604/cmes.2022.021880

Cited by 6 publications

(4 citation statements)

References 78 publications

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“…As one valuable computing method, surrogate modeling methods have emerged and attracted much attention in reliability design fields [54][55][56][57]. In the surrogate modeling method, the tremendous computing tasks of real limit state functions can be avoided by establishing a surrogate mathematical model, which is conducive to alleviating the computing burdens and improving the computing efficiency [58][59][60]. For instance, Meng et al [61] proposed an enhanced collaborative optimization method based on the adaptive surrogate model for the design of high-dimensional nonlinearity systems.…”

Section: Mcsmentioning

confidence: 99%

Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

Li,

Song

2024

CMES

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Fatigue reliability-based design optimization of aeroengine structures involves multiple repeated calculations of reliability degree and large-scale calls of implicit high-nonlinearity limit state function, leading to the traditional direct Monte Claro and surrogate methods prone to unacceptable computing efficiency and accuracy. In this case, by fusing the random subspace strategy and weight allocation technology into bagging ensemble theory, a random forest (RF) model is presented to enhance the computing efficiency of reliability degree; moreover, by embedding the RF model into multilevel optimization model, an efficient RF-assisted fatigue reliability-based design optimization framework is developed. Regarding the low-cycle fatigue reliability-based design optimization of aeroengine turbine disc as a case, the effectiveness of the presented framework is validated. The reliabilitybased design optimization results exhibit that the proposed framework holds high computing accuracy and computing efficiency. The current efforts shed a light on the theory/method development of reliability-based design optimization of complex engineering structures.

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Section: Mcsmentioning

confidence: 99%

Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

Li,

Song

2024

CMES

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“…For example, Zaidel et al proposed a neuromorphic algorithm based on nef for inverse kinematics and PID control in order to improve the motion accuracy of the 6-DOF robotic arm (Zaidel et al, 2021). In addition, in order to improve the motion accuracy and stability of the mechanism, Zhang's team not only proposed a new sampling method based on active Kriging model and adaptive importance sampling, but also proposed a multistage linkage method based on active extreme value Kriging (Zhang et al, 2022;Zhang et al, 2023). In recent years, with the continuous development of artificial intelligence technology, artificial intelligence technology has been widely used in various fields.…”

Section: Introductionmentioning

confidence: 99%

Dynamic modeling and optimization of an eight bar stamping mechanism based on RBF neural network PID control

Ma,

2024

Front. Mech. Eng.

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Introduction: Modern industrial manufacturing often requires the eight-bar stamping mechanism to have high motion accuracy and stability. To meet these stringent requirements, traditional control techniques such as proportional-integral-derivative (PID) control need to be improved.Methods: In this study, radial basis function neural network is introduced to improve the traditional proportional integral derivative control technique. The improved proportional integral derivative technique is applied to the modeling and optimization of eight kinds of bar stamping mechanisms.Results: Comparing the improved control technology, the experiment showed that the peak time and adjustment time of the improved technology were 0.516 s and 1.038 s, respectively, which are better than the comparative control technology. In addition, in the comparative analysis of the eight bar stamping mechanism, the proposed architecture scored 9.3 points in operational efficiency, which is significantly greater than the comparative architecture.Discussion: The results show that the combination of PID control strategy and radial basis function neural network provides a powerful tool for dynamic modeling and optimization of eight-bar stamping mechanism. It not only provides enhanced motion accuracy and stability, but also brings significant practicality to industrial manufacturing. This integration opens up new possibilities for improving the performance of complex mechanical systems to meet the evolving needs of modern manufacturing.

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“…Combining adaptive learning strategies with surrogate models, that is adaptive surrogate-model-based reliability evaluation is one of the ways to solve the above problems in reliability analysis field. A lot of valuable researches have performed on adaptive surrogate-model-based reliability evaluation method (Echard et al ., 2011; Marelli and Sudret, 2018; Luo et al ., 2022b; Yu et al ., 2022; Yu and Li, 2021; Meng et al ., 2015; Zhang et al ., 2022a, 2022b; Deng et al ., 2022). Echard et al .…”

Section: Introductionmentioning

confidence: 99%

A comparative study for adaptive surrogate-model-based reliability evaluation method of automobile components

Yang

Meng

Wang

et al. 2023

IJSI

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PurposeThis study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.Design/methodology/approachIn this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.FindingsBy comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.Originality/valueAlthough a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.

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Active Kriging-Based Adaptive Importance Sampling for Reliability and燬ensitivity Analyses of Stator Blade Regulator

Cited by 6 publications

References 78 publications

Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

Dynamic modeling and optimization of an eight bar stamping mechanism based on RBF neural network PID control

A comparative study for adaptive surrogate-model-based reliability evaluation method of automobile components

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