A stochastic process discretization method combing active learning Kriging model for efficient time-variant reliability analysis

Zhang, Dequan; Zhou, Pengfei; Jiang, Chen; Yang, Meide; Han, Xu; Li, Qing

doi:10.1016/j.cma.2021.113990

Cited by 66 publications

(11 citation statements)

References 62 publications

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“…, x s ] and the corresponding responses g 0 (x) are extracted. Based on the initial sample set D 0 = { x, g 0 (x)} and Kriging tool [68,69], the initial Kriging can be built as…”

Section: Mcmc Initial State Determinationmentioning

confidence: 99%

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

Zhang¹,

Song²,

Bai³

2023

Computer Modeling in Engineering &Amp; Sciences

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The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity, multi-failure regions, and small failure probability, which brings in unacceptable computing efficiency and accuracy of the current analysis methods. In this case, by fitting the implicit limit state function (LSF) with active Kriging (AK) model and reducing candidate sample pool with adaptive importance sampling (AIS), a novel AK-AIS method is proposed. Herein, the AK model and Markov chain Monte Carlo (MCMC) are first established to identify the most probable failure region(s) (MPFRs), and the adaptive kernel density estimation (AKDE) importance sampling function is constructed to select the candidate samples. With the best samples sequentially attained in the reduced candidate samples and employed to update the Kriging-fitted LSF, the failure probability and sensitivity indices are acquired at a lower cost. The proposed method is verified by two multi-failure numerical examples, and then applied to the reliability and sensitivity analyses of a typical stator blade regulator. With methods comparison, the proposed AK-AIS is proven to hold the computing advantages on accuracy and efficiency in complex reliability and sensitivity analysis problems.

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“…, x s ] and the corresponding responses g 0 (x) are extracted. Based on the initial sample set D 0 = { x, g 0 (x)} and Kriging tool [68,69], the initial Kriging can be built as…”

Section: Mcmc Initial State Determinationmentioning

confidence: 99%

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

Zhang¹,

Song²,

Bai³

2023

Computer Modeling in Engineering &Amp; Sciences

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“…Zhang et al. (2021a, b) studied an efficient method for nonlinear structures dynamic reliability analysis by the linear moments. To efficiently obtain the extreme value distribution, Hu and Du (2013a, b) employed a saddle point approximation to estimate the distributions of the extreme value.…”

Section: Dynamic Reliability Analysis Of Single-objective Structurementioning

confidence: 99%

“…, 2022; Zhang et al. , 2021a, b; Jiang et al. , 2019a, b) surrogate model, and the reliability is analyzed by combining the allowable value.…”

Section: Dynamic Reliability Analysis Of Single-objective Structurementioning

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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“…How to quantify the degradation process is the key to carry out the time-dependent reliability analysis of the CRDM. [16][17][18] F I G U R E 3 Structure of CRDM latch assembly. 1-conducting ring, 2-lift coil assembly, 3-drive rod, 4-guide tube, 5-latch housing, 6-movable latch coil assembly, 7-movable latch, 8-movable latch plunger, 9-stationary coil assembly, 10-stationary latch plunger, 11-stationary latch support, 12-stationary latch, 13-stationary armature, 14-stationary latch pole, 15-buffer shaft, 16-coil housing, 17-lift armature, 18-lift spring, 19-lift shim, and 20-lift pole.…”

Section: F I G U R E 1 Structural Illustration Of Crdmmentioning

confidence: 99%

Degradation‐based reliability analysis of magnetic jack type control rod drive mechanism

Chen

et al. 2022

Quality & Reliability Eng

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The magnetic jack type control rod drive mechanism (CRDM) is the key actuator in the nuclear power plant reactor. The latch assembly of the CRDM acts as the execution component to lift, insert, and release the control rod assembly to accomplish the reactor startup, power regulation, and shutdown, etc. The accuracy and the timeliness of CRDM latch assembly action will directly determine the safety operation of the reactor. Considering the time variation of the structural strength deterioration in the latch assembly, the time‐dependent reliability of CRDM's lift action is evaluated based on the Gamma degradation process. P‐S‐N curve is introduced to model the shape function and the scale parameter of the Gamma process. The stress subjected to the key structure of the latch assembly is calculated by a multi‐physics field coupling simulation scheme with utilization of MATLAB to transfer simulated results between the ANSYS and ADAMS software. Finally, the time‐dependent reliability is calculated based on the Monte Carlo simulation method. In this paper, the lift pole thread of the assembly is presented to demonstrate the effectiveness of the proposed method.

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A stochastic process discretization method combing active learning Kriging model for efficient time-variant reliability analysis

Cited by 66 publications

References 62 publications

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

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

Structural dynamic reliability analysis: review and prospects

Degradation‐based reliability analysis of magnetic jack type control rod drive mechanism

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