A Minimum Discrepancy Method for Weibull Distribution Parameter Estimation

Xie, Liyang; Wu, Ningxiang; Yang, Xiaoyu

doi:10.1142/s0219455423500852

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…Wu et al [ 25 ] combined EMWM with disc spring winding to create a novel sandwich structure, which retains favorable compressive and energy dissipation properties even under high‐temperature conditions. Xue et al [ 26,27 ] conducted an analysis of the mechanical properties and vibration characteristics of SCS‐EMWM under various gradient densities in thermal environments. Wei et al [ 28 ] discovered that the compression and shear properties of the EMWM sandwich structure with brazing technology remain nearly unchanged even after undergoing hundreds of thermal cycles.…”

Section: Introductionmentioning

confidence: 99%

Thermo‐Mechanical Characteristics of a Sandwich Cylindrical Shell with Entangled Metallic Wire Mesh: Numerical and Experimental Analysis

Xue,

Lin,

Wei

et al. 2023

Adv Eng Mater

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As a novel lightweight composite structure with notable mechanical resistance and thermal insulation characteristics in high‐temperature environments, the sandwich cylindrical shell with entangled metallic wire mesh (SCS‐EMWM) has been growing interest in industrial applications. This study focuses on investigating the thermo‐mechanical behavior of the SCS‐EMWM in high‐temperature environments. The static mechanical properties of entangled metal wire mesh (EMWM) are first characterized, and the macroscopic mechanical properties are identified using the Ogden–Mullins model. Subsequently, the deformation modes at different densities and temperatures are analyzed through theoretical and finite element methods. The radial compression test of SCS‐EMWM is performed to characterize its energy dissipation (ΔW), loss factor (η), and secant stiffness (K). The quasi‐static loading‐unloading results of the EMWM reveal a positive correlation between temperature, density, and the mechanical behavior. In addition, as the density and temperature increase, the peak load of SCS‐EMWM continuously increases from 20 to 200 N. Under high‐temperature conditions, the radial compression test of SCS‐EMWM reveals a significant concurrence between experimental and simulation results regarding load variations. Furthermore, in comparison to EMWM, the loss factor of SCS‐EMWM exhibits an approximate increase of 0.1 and shows a positive correlation with temperature, while it decreases with increasing density.

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Section: Introductionmentioning

confidence: 99%

Thermo‐Mechanical Characteristics of a Sandwich Cylindrical Shell with Entangled Metallic Wire Mesh: Numerical and Experimental Analysis

Xue,

Lin,

Wei

et al. 2023

Adv Eng Mater

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Effect Analysis of Probability Estimators on Parameter Estimation of the Three-Parameter Weibull Distribution

Yang

Xie

Song

et al. 2023

Int. J. Str. Stab. Dyn.

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The three-parameter Weibull distribution is one of the most widely used probabilistic models for the characterization of fatigue failure data. The linear regression estimation (LRE), the minimum discrepancy estimation (MDE), errors-in-variables estimation (EIV) as well as the least squares estimation (LSE) are generally applied in the estimation of Weibull parameters for their simplicity, in which probability estimators play an important role. In this paper, compared with five commonly used estimators, an optimal probability estimator with the four methods for different sample sizes is obtained by Monte Carlo simulations. The optimal probability estimator shows more robustness and higher accuracy. In conclusion, an optimal probability estimator of the midpoint rank is recommended for LRE, MDE and EIV especially with small samples in practical applications.

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Fine-tuning weibull distribution parameters in Morocco’s Tarfaya and Tangier wind farms using two-stage swarm optimization

Douiri

2024

Wind Engineering

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This study assesses the efficacy of particle swarm optimization (PSO) in estimating scale ( c) and shape ( k) parameters of the Weibull distribution model for wind energy forecasting at two key wind farm sites in Morocco— Tarfaya in the south and Tangier in the north, utilizing real wind data from 2022. Employing a novel square frequency error objective function to enhance parameter accuracy, the study adopts a two-stage training approach involving recursive least-square estimation and PSO fine-tuning. Validation with artificial data underscores PSO’s effectiveness under diverse wind conditions. Parameter sensitivity analysis identifies four optimal PSO configurations, with the PSO-4 model exhibiting superior performance. Comparative analysis against traditional and heuristic optimization methods consistently demonstrates PSO-4’s lowest root-mean-square error (RMSE) and mean absolute error (MAE), high coefficients of determination ( R2), and shortest computation time. The research highlights PSO-4 model as a precise and efficient tool for Weibull distribution parameter estimation in wind energy forecasting, showcasing robust convergence across both wind farm sites.

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A Minimum Discrepancy Method for Weibull Distribution Parameter Estimation

Cited by 5 publications

References 26 publications

Thermo‐Mechanical Characteristics of a Sandwich Cylindrical Shell with Entangled Metallic Wire Mesh: Numerical and Experimental Analysis

Thermo‐Mechanical Characteristics of a Sandwich Cylindrical Shell with Entangled Metallic Wire Mesh: Numerical and Experimental Analysis

Effect Analysis of Probability Estimators on Parameter Estimation of the Three-Parameter Weibull Distribution

Fine-tuning weibull distribution parameters in Morocco’s Tarfaya and Tangier wind farms using two-stage swarm optimization

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